
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import plotly.express as px

import plotly.graph_objects as go

from plotly.offline import init_notebook_mode, iplot

init_notebook_mode()


# remove empty columns


pd.options.display.float_format = '{:,.1f}'.format
pd.set_option('display.max_rows', None)


protection= pd.read_csv(r'world protected forests procent.csv')


protection.head()


protection= protection.rename(columns= {'Unnamed: 0': 'Country'})


protection.iloc[41,2]= 1


protection= protection.rename(columns= {'% protected forest out fo total forest area': '% protected forest out of total forest area'})


protection= protection.rename(columns= {'% protected forest out of total forest area': '% Protected Forest'})
protection= protection.rename(columns= {'total forest area 2020 1000 ha': 'Forest Area 2020 (1000 ha)'})
protection= protection.rename(columns= {'% forest of total land area': '% Forest'})
protection= protection.rename(columns= {'% all nature protected areas out of total surface': '% Protected Nature Areas'})


import geojson
import pandas as pd
import plotly.graph_objects as go
import geopandas as gpd


europe_geojson = gpd.read_file("choropleth map protection\europe.geojson")


europe_geojson.head()


protection['Country'].unique()

array(['Finland', 'Sweden', 'Japan', 'Montenegro', 'Brazil', 'Slovenia',
       'Estonia', 'Latvia', 'Russian Federation', 'Austria', 'Belarus',
       'Bosnia and Herzegovina', 'Liechtenstein', 'Georgia',
       'Slovak Republic', 'Norway', 'Spain', 'Luxembourg', 'Portugal',
       'United States', 'Bulgaria', 'Lithuania', 'Croatia',
       'Czech Republic', 'Andorra', 'Italy', 'Germany', 'Switzerland',
       'France', 'Serbia', 'Poland', 'Greece', 'Romania', 'Turkiye',
       'Albania', 'Belgium', 'Hungary', 'Cyprus', 'Ukraine', 'Denmark',
       'United Kingdom', 'Moldova', 'Ireland', 'Netherlands', 'Malta',
       'Iceland', 'Monaco'], dtype=object)


protection['Country']= protection['Country'].str.replace('Czech Republic', 'Czechia')


protection['Country']= protection['Country'].str.replace('Bosnia and Herzegovina', 'Bosnia and Herz.')


protection['Country']= protection['Country'].str.replace('Turkiye', 'Turkey')
protection['Country']= protection['Country'].str.replace('Russian Federation', 'Russia')


protection['Country']= protection['Country'].str.replace('Slovak Republic', 'Slovakia')
protection['Country']= protection['Country'].str.replace('Russian Federation', 'Russia')


list(set(list(europe_geojson['name'].unique())) - set(list(protection['Country'])))

['Åland',
 'Gibraltar',
 'Guernsey',
 'San Marino',
 'Kosovo',
 'Jersey',
 'Isle of Man',
 'Faeroe Is.',
 'Vatican',
 'North Macedonia']


list(set(list(protection['Country']))- set(list(europe_geojson['name'].unique())) )

['United States', 'Brazil', 'Cyprus', 'Turkey', 'Japan', 'Georgia']


import json
back_geojson = europe_geojson.to_json()
j = json.loads(back_geojson)


['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',
             'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg',
             'brwnyl', 'bugn', 'bupu', 'burg', 'burgyl', 'cividis', 'curl',
             'darkmint', 'deep', 'delta', 'dense', 'earth', 'edge', 'electric',
             'emrld', 'fall', 'geyser', 'gnbu', 'gray', 'greens', 'greys',
             'haline', 'hot', 'hsv', 'ice', 'icefire', 'inferno', 'jet',
             'magenta', 'magma', 'matter', 'mint', 'mrybm', 'mygbm', 'oranges',
             'orrd', 'oryel', 'oxy', 'peach', 'phase', 'picnic', 'pinkyl',
             'piyg', 'plasma', 'plotly3', 'portland', 'prgn', 'pubu', 'pubugn',
             'puor', 'purd', 'purp', 'purples', 'purpor', 'rainbow', 'rdbu',
             'rdgy', 'rdpu', 'rdylbu', 'rdylgn', 'redor', 'reds', 'solar',
             'spectral', 'speed', 'sunset', 'sunsetdark', 'teal', 'tealgrn',
             'tealrose', 'tempo', 'temps', 'thermal', 'tropic', 'turbid',
             'turbo', 'twilight', 'viridis', 'ylgn', 'ylgnbu', 'ylorbr',
             'ylorrd'].
        Appending '_r' to a named colorscale reverses it.

  File "C:\Users\mihai\AppData\Local\Temp/ipykernel_8868/3905000332.py", line 15
    'ylorrd'].
              ^
SyntaxError: invalid syntax


protection.loc[1, '% Protected Forest']= 6.8


list(set(list(europe_geojson['name'].unique())) - set(list(protection['Country'])))

['Åland',
 'Gibraltar',
 'Guernsey',
 'San Marino',
 'Kosovo',
 'Jersey',
 'Isle of Man',
 'Faeroe Is.',
 'Vatican',
 'North Macedonia']


protection.loc[47]= protection.loc[46]
protection.loc[48]= protection.loc[46]


protection.iloc[47, 0]= 'Kosovo'
protection.iloc[47, 1]= 1089
protection.iloc[47, 3]= 44.7
protection.iloc[47, 2]= 42.6
protection.iloc[47, 4]= 4.39

protection.iloc[48, 0]= 'North Macedonia'
protection.iloc[48, 1]= 989
protection.iloc[48, 3]= 38.5
protection.iloc[48, 2]= 13.1
protection.iloc[48, 4]= 8.9


# europe_geojson = gpd.read_file("choropleth map protection\custom.geojson")


import json
back_geojson = europe_geojson.to_json()
j = json.loads(back_geojson)


import plotly.offline as pyo
import plotly.graph_objs as go
# Set notebook mode to work in offline
pyo.init_notebook_mode()


import geojson
import pandas as pd
import plotly.graph_objects as go

# with open("file.geojson", "r", encoding="utf-8") as f:
#     geometry = geojson.load(f)
import plotly.express as px

fig = px.choropleth(protection, geojson=j, locations='Country', color='% Protected Forest',featureidkey="properties.name",
                           color_continuous_scale= [[0, 'rgb(254, 125, 74)'], [0.2, 'yellow'],[0.4, 'rgb(221, 247, 180)'], [1, 'green']],
#                            range_color=(0, 12),
                           scope="europe",
                    projection="hammer",
                           labels={'% Protected Forest':'% Protected Forest'}
                          )

fig = px.choropleth_mapbox(protection, geojson=j, locations='Country', color='% Protected Forest',featureidkey="properties.name",
                           color_continuous_scale= [[0, 'rgb(254, 125, 74)'], [0.2, 'yellow'],[0.4, 'rgb(221, 247, 180)'], [1, 'green']],
#                            range_color=(0, 12),
#                            scope="europe",
#                     projection="hammer",
                           labels={'% Protected Forest':'% Protected Forest'},
                          
                           mapbox_style="carto-positron",
                           zoom=3, center = {"lat": 58, "lon": 15},
                           opacity=0.7,
                           
                          )
fig.update_layout(autosize=True,
    width=1000,
    height=900,
    margin=dict(
        l=10,
        r=10,
        b=100,
        t=100,
        pad=1))
fig.update_layout(
    title={
        'text': "<b>Protected Forest out of Total Forested Area (%)",
        'y':0.95,
        'x':0.45,
        'xanchor': 'center',
        'yanchor': 'top'})
fig.show()


import pandas as pd
protected_forest_county= pd.read_csv(r'graphs\Protected forest by county.csv')


protected_forest_county= protected_forest_county.rename(columns= {'region': 'County'})


protected_forest_county.head()


# sort by total land area
protected_forest_county= protected_forest_county.sort_values(by= 'All Land Areal')


# cleanup the County name

import re
protected_forest_county['County']  =  protected_forest_county['County'].str[3:]


protected_forest_county['County']  =  protected_forest_county['County'].str.replace('s län', '')


protected_forest_county['County']  =  protected_forest_county['County'].str.replace(' län', '')


protected_forest_county= protected_forest_county.rename(columns= {'region': 'County'})
protected_forest_county= protected_forest_county.rename(columns= {'Productive forest inside formally protected areas that ARE in fact protected agains cutting': 'Protected Productive Forest'})

protected_forest_county= protected_forest_county.rename(columns= {'Total Forest Area 2021': 'Total Forest Area'})


protected_forest_county.columns

Index(['County', 'All Land Areal', 'Protected areal i hektar 2021',
       'Protected Landareal i hektar 2021', 'protected Water areal ',
       'Total Forest Areal per County', 'Total Productive Forest Area',
       'Total Improductive Forest Area',
       'Total productive forest inside nature protected areas',
       'NOT PROTECTED Productive forest inside nature protected areas ',
       'PROTECTED Productive forest inside formally protected areas ',
       'Formal procent of protected land', '% Forest in Total Land Area',
       '% Productive Forest in Total Area',
       '% Protected Forest out of Total Productive Forest'],
      dtype='object')


import plotly.graph_objects as go

protected_forest_county= protected_forest_county.sort_values(by= 'Total Forest Areal per County', ascending = True)

# fig = px.bar(protected_forest_county, y="County", x=["All Land Areal", "Total Forest Area 2021", "Total Productive Forest Area",'Protected Productive Forest'], 
#              title="Protected Forest Area by County (hectares) - 2021",
#             barmode='overlay',
#             orientation='h', opacity=0.7)
fig = go.Figure()
fig.add_trace(go.Bar(x=protected_forest_county["PROTECTED Productive forest inside formally protected areas "],
                y= protected_forest_county["County"],
                name='Protected Productive Forest',
                marker_color='red', orientation= 'h', legendrank=3
                ))
fig.add_trace(go.Bar(x =protected_forest_county["Total Productive Forest Area"],
                y= protected_forest_county["County"],
                name='Total Productive Forest',
                marker_color='rgb(197, 219, 204)', orientation= 'h', legendrank=2
                ))

fig.add_trace(go.Bar(x=protected_forest_county["Total Forest Areal per County"],
                y= protected_forest_county["County"],
                name='Total Forest Areal per County',
                marker_color='green', orientation= 'h', legendrank=1
                ))

# fig.add_trace(go.Bar(x=protected_forest_county["All Land Areal"],
#                 y= protected_forest_county["County"],
#                 name='Rest of world',
#                 marker_color='gray', orientation= 'h'
#                 ))
fig.update_layout(height=700, width= 600,legend=dict(
        x=0.4,
        y=0.02), paper_bgcolor='rgba(0,0,0,0)',
    plot_bgcolor='rgba(0,0,0,0)', title= '<b>Productive Forest Protected Area (hectares) - 2021', title_x=0.5)

fig.show()


all_land_protected = pd.read_csv(r'graphs\all land versus protected areas pie chart.csv')


all_land_protected


all_land_protected = pd.read_csv(r'graphs\all land versus protected areas pie chart_simplified.csv')


all_land_protected


all_land_protected['order']= [5, 3, 4,6, 7 , 2,1]


all_land_protected= all_land_protected.sort_values(by= 'order')


labels = all_land_protected['Nature Type']
values = all_land_protected['2021']

color_discrete_map= ['rgb(170, 201,197 )', 'rgb(105,186,110 )', 'rgb(132, 146, 132)','rgb(239,166,82)',
                     'rgb(102,255,255)','rgb(156, 216, 126)', 'rgb(0,76,153)']  


# color_discrete_map={'All other land':'rgb(170, 201,197 )',
#                                  'Forest Area':'rgb(146, 201,161 )',
#                                  'Improductive forest*':'rgb(132, 146, 132)',
#                                  'Productive forest*':'rgb(132, 146, 132)',
#                                    'Land above the alpine border*': 'rgb(175, 222, 216)',
#                                    'Land below the alpine border*': 'rgb(156, 216, 126)',
#                                 'Sea plus Inland Waters*': 'rgb(81, 192, 247)'}



trace2 = go.Pie(
    labels=labels, values=values,sort= False, direction= 'clockwise', 
#                              hole= 0.3, 
                             pull=[0, 0, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
    textinfo='label',
    textposition='outside',  marker=dict(colors=color_discrete_map))

trace1 = go.Pie(
    labels=labels, values=values,sort= False, direction= 'clockwise', 
#                              hole= 0.3, 
                             pull=[0, 0, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],  textinfo='percent',
    textposition='inside',  marker=dict(colors=color_discrete_map 
#                                         ,line=dict(color='#000000', width=[0, 0,2,2,2,2,2,2])
                                       ))
                                                                             

fig = go.Figure(data=[trace2,trace1] )
fig.update_layout(showlegend=False)
fig.update_layout(uniformtext_minsize=14, uniformtext_mode='hide', height=850, width= 850)

fig.update_traces(rotation=160)

fig.update_layout(
    title={
        'text': "<b>Land Use in Sweden with Protected Areas(*) by Type",
        'y':0.85,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'})

fig.show()
# iplot([trace1, trace2], filename='basic_pie_chart')


productive= ['Yearly growth at least 1 cubic meter per year/hectare due to favorable natural conditions', 
             'Tree cover average is 70%','Set on accessible terrain which make exploitation easier',
             'Contains 80% of the total CO2 sink stored by protected areas',
            'The large majority of forest species, including the threathned ones, live in a productive forest environment',
             'At least 1 400 threathened species are directly impacted by the current clear-cutting practices in Sweden',
             'Only 3.5% of the productive forest in Sweden is protected de facto, even if they lie within a formally protected area']
improductive= ['Slow growth, less than 1 cubic metre per year/hectare due to harsher natural conditions',
               'Tree cover at least 10% of the surface' ,
               'Natural impediments such as mires and stony areas with thin cover are counted under this form, which may well not even be considered forest**',
               'Contains 20% of the total CO2 sink stored by protected areas',
               'Offers little or no support for various species of plants or animals', 
               'Only 2% of the forest dependent species thrive in this environment', 
              'In Sweden a good portion of the protected forests are in fact improductive and they are situated in the alpine region']
# https://www.artdatabanken.se/det-har-gor-vi/rodlistning/Sammanfattning-rodlista-2020/#:~:text=B%C3%A5de%20bland%20de%20skogslevande%20arterna,att%20skogslevande%20arter%20blir%20r%C3%B6dlistade.               
#  reference: https://www.artdatabanken.se/arter-och-natur/Dagens-natur/manga-skogslevande-arter-hotas-av-trakthyggesbruk/


import plotly.graph_objects as go
fig = go.Figure(data=[go.Table(header=dict(values=['<b>Productive Forest', '<b>Improductive Forest'],
                                          line_color='darkslategray'),
                 cells=dict(values=[productive, improductive], 
                            align= 'left', 
                            line_color='darkslategray',
                            fill_color='white',
                            font=dict( size=13), 
                          ))
                               ])
fig.update_layout (height=700, width= 700)


# fig.table.cells.Fill`(color= 'white')
fig.show()


categories = ['Tree cover','Tree growth','Soil quality', 'Acessibility', 'Potential for Biodiversity', 'Protection Status', 'CO2 sink']

fig = go.Figure()

fig.add_trace(go.Scatterpolar(
      r=[4, 5, 4, 5, 5, 0.3, 5],
      theta=categories,
      fill='toself',
      name='Productive Forests'
))
fig.add_trace(go.Scatterpolar(
      r=[1, 1, 1, 1, 2, 3, 1],
      theta=categories,
      fill='toself',
      name='Improductive Forests'
))

fig.update_layout(
  polar=dict(
    radialaxis=dict(
      visible=False,
      range=[0, 5]
    )),
  showlegend=True
)

fig.update_layout(legend=dict(
    yanchor="bottom",
    y=1,
    xanchor="right",
    x=0.3))

fig.show()


import geopandas as gpd
sweden_geojson = gpd.read_file(r'C:\Users\mihai\Documents\monica\Dataquest\TUTORIAL\portofoliu\recycling sweden\Choropleth map\data\sweden-counties_1680.geojson')


protected_forest_county= pd.read_csv(r'graphs\Protected forest by county.csv')


protected_forest_county.head()


protected_forest_county['region']= protected_forest_county['region'].str[3:]


protected_forest_county['region']= protected_forest_county['region'].str.replace('s län', '')


protected_forest_county['region']= protected_forest_county['region'].str.replace(' län', '')


protected_forest_county['region']= protected_forest_county['region'].str.replace('Örebro', 'Orebro')


missing= list(set(list(sweden_geojson['NAME_1'].unique())) - set(list(protected_forest_county['region'])))


missing

[]


protected_forest_county= protected_forest_county.rename(columns= {'region': 'County'})


import json
back_geojson = sweden_geojson.to_json()
j = json.loads(back_geojson)

j

{'type': 'FeatureCollection',
 'features': [{'id': '0',
   'type': 'Feature',
   'properties': {'ISO': 'SWE',
    'NAME_0': 'Sweden',
    'ID_1': 1,
    'NAME_1': 'Östergötland',
    'HASC_1': 'SE.OG',
    'TYPE_1': 'Laen|län',
    'ENGTYPE_1': 'County',
    'VARNAME_1': 'Östergötlands län'},
   'geometry': {'type': 'MultiPolygon',
    'coordinates': [[[[16.820663, 58.120609],
       [16.78573, 58.11423],
       [16.800645, 58.092453],
       [16.845783, 58.090693],
       [16.820663, 58.120609]]],
     [[[16.924676, 58.287568],
       [16.886211, 58.27569],
       [16.859128, 58.257652],
       [16.884248, 58.246433],
       [16.912901, 58.264251],
       [16.924676, 58.287568]]],
     [[[16.890136, 58.377317],
       [16.867371, 58.3496],
       [16.886211, 58.3485],
       [16.890136, 58.377317]]],
     [[[16.813598, 58.418671],
       [16.788085, 58.407893],
       [16.779057, 58.379516],
       [16.80457, 58.368738],
       [16.806925, 58.339042],
       [16.833615, 58.335742],
       [16.832438, 58.321224],
       [16.880323, 58.315505],
       [16.874828, 58.332662],
       [16.82027, 58.361699],
       [16.857951, 58.386776],
       [16.823803, 58.405693],
       [16.813598, 58.418671]]],
     [[[16.912116, 58.529537],
       [16.899556, 58.514799],
       [16.941947, 58.50732],
       [16.961964, 58.482683],
       [16.991795, 58.485983],
       [16.963142, 58.5071],
       [16.925854, 58.516559],
       [16.912116, 58.529537]]],
     [[[16.192655, 58.632704],
       [16.187553, 58.615986],
       [16.22484, 58.605428],
       [16.248783, 58.622146],
       [16.192655, 58.632704]]],
     [[[16.741377, 58.626765],
       [16.709191, 58.655142],
       [16.673081, 58.672079],
       [16.63854, 58.671199],
       [16.628335, 58.684398],
       [16.57574, 58.692097],
       [16.463483, 58.689017],
       [16.384982, 58.695836],
       [16.375562, 58.704415],
       [16.370852, 58.74929],
       [16.352012, 58.762268],
       [16.307266, 58.774366],
       [16.305304, 58.792404],
       [16.278221, 58.805163],
       [16.266838, 58.82738],
       [16.196973, 58.829799],
       [16.186375, 58.847397],
       [16.140059, 58.872914],
       [16.096099, 58.876214],
       [16.083146, 58.916248],
       [16.002683, 58.931866],
       [15.966572, 58.944185],
       [15.931247, 58.947484],
       [15.884931, 58.968602],
       [15.873156, 58.995218],
       [15.818598, 59.011496],
       [15.812318, 58.993239],
       [15.787198, 58.98334],
       [15.788768, 58.969922],
       [15.739312, 58.945725],
       [15.72636, 58.98136],
       [15.691819, 58.975641],
       [15.684362, 58.966402],
       [15.640794, 58.965082],
       [15.622346, 58.973441],
       [15.598796, 58.954524],
       [15.565433, 58.940005],
       [15.597225, 58.896011],
       [15.572105, 58.890732],
       [15.54149, 58.857956],
       [15.446111, 58.854656],
       [15.459849, 58.814621],
       [15.417851, 58.804283],
       [15.414318, 58.831119],
       [15.3786, 58.838818],
       [15.34563, 58.82408],
       [15.316585, 58.845637],
       [15.290679, 58.844538],
       [15.280082, 58.857736],
       [15.200403, 58.805163],
       [15.147808, 58.807802],
       [15.123865, 58.793284],
       [15.12779, 58.766447],
       [15.077157, 58.755449],
       [15.032804, 58.699796],
       [14.956658, 58.687697],
       [14.904847, 58.685718],
       [14.889147, 58.676039],
       [14.830664, 58.660201],
       [14.804759, 58.659101],
       [14.781601, 58.640183],
       [14.72037, 58.58805],
       [14.708595, 58.551535],
       [14.711342, 58.533717],
       [14.672484, 58.505121],
       [14.666597, 58.486643],
       [14.635197, 58.467505],
       [14.629309, 58.449028],
       [14.582993, 58.415592],
       [14.577106, 58.397334],
       [14.545705, 58.377977],
       [14.523725, 58.35444],
       [14.481727, 58.294167],
       [14.482905, 58.285148],
       [14.441299, 58.224656],
       [14.425599, 58.214977],
       [14.413431, 58.182861],
       [14.461709, 58.148986],
       [14.465242, 58.126548],
       [14.51038, 58.110491],
       [14.580246, 58.100152],
       [14.590058, 58.091573],
       [14.632841, 58.088933],
       [14.760013, 58.094213],
       [14.771396, 58.076615],
       [14.889932, 58.081674],
       [14.895035, 58.104331],
       [14.920547, 58.105431],
       [14.908772, 58.127428],
       [14.942528, 58.128748],
       [14.975498, 58.139087],
       [14.977853, 58.121049],
       [15.015141, 58.100152],
       [15.028486, 58.064516],
       [15.049681, 58.03394],
       [15.078727, 58.007984],
       [15.081867, 57.985766],
       [15.040261, 57.979607],
       [15.014356, 57.982907],
       [15.019459, 57.947271],
       [14.995516, 57.937152],
       [15.013964, 57.924394],
       [14.999833, 57.905916],
       [15.039084, 57.866981],
       [15.041439, 57.848944],
       [15.076372, 57.841465],
       [15.070484, 57.823207],
       [15.095212, 57.765574],
       [15.124257, 57.739618],
       [15.12779, 57.712781],
       [15.1639, 57.696283],
       [15.188236, 57.701562],
       [15.231019, 57.698703],
       [15.297352, 57.705742],
       [15.324435, 57.693423],
       [15.34877, 57.698703],
       [15.424916, 57.697163],
       [15.448859, 57.706842],
       [15.444541, 57.742697],
       [15.469661, 57.743797],
       [15.509697, 57.758755],
       [15.543452, 57.759855],
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       [11.181548, 58.782725],
       [11.198033, 58.764688],
       [11.160745, 58.753249],
       [11.18037, 58.740491],
       [11.168988, 58.724213],
       [11.216481, 58.709475],
       [11.218051, 58.692097],
       [11.265151, 58.66768],
       [11.278496, 58.619946],
       [11.279281, 58.570892],
       [11.235321, 58.548455],
       [11.256516, 58.520739],
       [11.305972, 58.493022],
       [11.317354, 58.465965],
       [11.261226, 58.445068],
       [11.234536, 58.439789],
       [11.221976, 58.395794],
       [11.256909, 58.375997],
       [11.219228, 58.369398],
       [11.236891, 58.355759],
       [11.267114, 58.35158],
       [11.303224, 58.373577],
       [11.33855, 58.363458],
       [11.37309, 58.385456],
       [11.387613, 58.368958],
       [11.35739, 58.34982],
       [11.405668, 58.34784],
       [11.39978, 58.316604],
       [11.442563, 58.294607],
       [11.414696, 58.27019],
       [11.477104, 58.279649],
       [11.533232, 58.318364],
       [11.542652, 58.335962],
       [11.574837, 58.364338],
       [11.561885, 58.374897],
       [11.56738, 58.399314],
       [11.600743, 58.413832],
       [11.628218, 58.400194],
       [11.630966, 58.382596],
       [11.597995, 58.361699],
       [11.571305, 58.328923],
       [11.536764, 58.309565],
       [11.525382, 58.282069],
       [11.503009, 58.278549],
       [11.465721, 58.251493],
       [11.431573, 58.242694],
       [11.405668, 58.203539],
       [11.415088, 58.197819]]]]}}]}


import geojson
import pandas as pd
import plotly.graph_objects as go

# with open("file.geojson", "r", encoding="utf-8") as f:
#     geometry = geojson.load(f)
import plotly.express as px

# fig = px.choropleth(protected_forest_county, geojson=j, locations='County', color='% Protected Forest out of Total Productive Forest',
#                     featureidkey="properties.NAME_1",
#                            color_continuous_scale= [[0, 'rgb(254, 125, 74)'], [0.2, 'yellow'],[0.4, 'rgb(221, 247, 180)'], [1, 'green']],
# #                            range_color=(0, 12),
# #                            scope="europe",
#                     projection="hammer",
#                            labels={'% Protected Forest out of Total Productive Forest': '% Protected Forest out of Total Productive Forest'}
#                           )

fig = px.choropleth_mapbox(protected_forest_county, geojson=j, locations='County', 
                           color='% Protected Forest out of Total Productive Forest',
                           color_continuous_scale="rdylgn",
                           featureidkey="properties.NAME_1",
                           range_color=(0, 10),
                           mapbox_style="carto-positron",
                           zoom=4, center = {"lat": 63, "lon": 14},
                           opacity=0.7,
                           labels={}
                          )


fig.update_layout(autosize=True,
    width=900,
    height=800,
    margin=dict(
        l=50,
        r=50,
        b=100,
        t=100,
        pad=1))
fig.update_layout(
    title={
        'text': "<b>% Protected Forest out of Total Productive Forest",
        'y':0.95,
        'x':0.35,
        'xanchor': 'center',
        'yanchor': 'top'})
fig.update_traces(showlegend=False, showscale=False)

# fig.update_layout(coloraxis_showscale=False)
fig.show()


import plotly.graph_objects as go

protected_forest_county= protected_forest_county.sort_values(by= 'Total Forest Areal per County', ascending = True)

# fig = px.bar(protected_forest_county, y="County", x=["All Land Areal", "Total Forest Area 2021", "Total Productive Forest Area",'Protected Productive Forest'], 
#              title="Protected Forest Area by County (hectares) - 2021",
#             barmode='overlay',
#             orientation='h', opacity=0.7)
fig = go.Figure()
fig.add_trace(go.Bar(x=protected_forest_county["PROTECTED Productive forest inside formally protected areas "],
                y= protected_forest_county["County"],
                name='Protected Productive Forest',
                marker_color='red', orientation= 'h', legendrank=3
                ))
fig.add_trace(go.Bar(x =protected_forest_county["Total Productive Forest Area"],
                y= protected_forest_county["County"],
                name='Total Productive Forest',
                marker_color='rgb(197, 219, 204)', orientation= 'h', legendrank=2
                ))

fig.add_trace(go.Bar(x=protected_forest_county["Total Forest Areal per County"],
                y= protected_forest_county["County"],
                name='Total Forest Areal per County',
                marker_color='green', orientation= 'h', legendrank=1
                ))

# fig.add_trace(go.Bar(x=protected_forest_county["All Land Areal"],
#                 y= protected_forest_county["County"],
#                 name='Rest of world',
#                 marker_color='gray', orientation= 'h'
#                 ))
fig.update_layout(height=700, width= 800,legend=dict(
        x=0.4,
        y=0.02), paper_bgcolor='rgba(0,0,0,0)',
    plot_bgcolor='rgba(0,0,0,0)', title= '<b>Productive Forest Protected Area (hectares) - 2021', title_x=0.5)

fig.show()


import pandas as pd
cumulative_clear= pd.read_csv(r'graphs\cumulative avverkning.csv')


cumulative_clear= cumulative_clear.fillna(0)


cumulative_clear.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 65 entries, 0 to 64
Data columns (total 9 columns):
 #   Column                                                                  Non-Null Count  Dtype  
---  ------                                                                  --------------  -----  
 0   Unnamed: 0                                                              65 non-null     int64  
 1   Existing kalhygge 1000 ha                                               65 non-null     float64
 2   yearly clearcut 1000ha                                                  65 non-null     float64
 3   Clear-cut Area, cumulative                                              65 non-null     float64
 4   Total Productive Forest                                                 65 non-null     int64  
 5   Forests not yet clear-cut in the last 70 years*                         65 non-null     float64
 6   Protected Productive Forest nside National Parks and Nature Reserves    65 non-null     int64  
 7   Protected Improductive Forest nside National Parks and Nature Reserves  65 non-null     int64  
 8   Voluntary Setasides**                                                   65 non-null     float64
dtypes: float64(5), int64(4)
memory usage: 4.7 KB


cumulative_clear.head()


cumulative_clear= cumulative_clear.rename(columns= {'Unnamed: 0': 'Year'})


# cumulative_clear= cumulative_clear.rename(columns= {'Cumulative area': 'Clear-cut Area, cumulative', 'total produktiv forest': 'Total Productive Forest', ''})


import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(go.Scatter(x= cumulative_clear['Year'], y=cumulative_clear['Total Productive Forest'], name= 'Total Productive Forest', fill='tozeroy', fillcolor= 'rgb(192,192,192)', mode= 'none', legendrank=5)) # fill to trace0 y
fig.add_trace(go.Scatter(x= cumulative_clear['Year'], y=cumulative_clear['Forests not yet clear-cut in the last 70 years*'], name= 'Natural Forest*', fill='tozeroy', fillcolor='rgb(0,150,20 )', mode= 'none', legendrank=4)) # fill down to 
fig.add_trace(go.Scatter(x= cumulative_clear['Year'], y=cumulative_clear['Protected Productive Forest nside National Parks and Nature Reserves'], name= 'Protected Productive Forest', fill='tozeroy', fillcolor= 'lime', mode= 'none', legendrank=3)) # fill to trace0 y
fig.add_trace(go.Scatter(x= cumulative_clear['Year'], y=cumulative_clear['Protected Improductive Forest nside National Parks and Nature Reserves'], name= 'Protected Improductive Forest', fill='tonexty', mode= 'none', legendrank=2)) # fill to trace0 y
fig.add_trace(go.Scatter(x= cumulative_clear['Year'], y=cumulative_clear['Voluntary Setasides**'], fill='tonexty', name= 'Voluntary set-asides**',  mode= 'none', legendrank=1)) # fill to trace0 y
fig.update_layout(height=600, width= 800,  
#                   legend=dict(
#         x=1.1,
#         y=0.9), 
                  paper_bgcolor='rgba(0,0,0,0)',
    plot_bgcolor='rgba(0,0,0,0)', 
                  title= '<b>From Natural Forests to Intensive Exploitation', title_x=0.5, title_y= 0.9,
                 yaxis_title="Surface (1000 hectares)")

fig.add_annotation(x=1960, y=8000,
            text="Natural Forest",
            showarrow=False,
                 font=dict(
#             family="Courier New, monospace",
            size=16,
            color="#ffffff"))

fig.add_annotation(x=1995, y=17000,
            text="Clear-cut and replanted",
            showarrow=False,
                 font=dict(
#             family="Courier New, monospace",
            size=16,
            color="black"))

fig.add_annotation(x=2009, y=500,
            text="Protected forests",
            showarrow=True,
                 font=dict(
#             family="Courier New, monospace",
            size=14,
            color="black"))
fig.update_traces(showlegend=False)

fig.write_html("pics/natural_intensive.html")
fig.write_image("pics/fig1.png")
fig.show()


pip install -U kaleido

Collecting kaleidoNote: you may need to restart the kernel to use updated packages.
  Downloading kaleido-0.2.1-py2.py3-none-win_amd64.whl (65.9 MB)
     ---------------------------------------- 65.9/65.9 MB 2.3 MB/s eta 0:00:00
Installing collected packages: kaleido
Successfully installed kaleido-0.2.1


# var 1 
Dual axis chart:
    - bar chart for annual volumes, split into Annual Growth and annual harvest, maybe add natural losses?
    - % utilization rate= Fellings out of Net annual increment


utilisation= pd.read_csv(r'graphs\growth versus avgång volym.csv')


utilisation.head()


import plotly.graph_objects as go
from plotly.subplots import make_subplots


fig = go.Figure()
fig = make_subplots(specs=[[{"secondary_y": True}]])

fig.add_trace(go.Bar(marker=dict(color='rgb(150,150,150)'),
                     x=utilisation['Year'], 
                     y= utilisation['Total Annual Growth millions m3'], 
                     name="Annual Growth (mil. m3)"),
             
                     secondary_y=False 
            
             )
# fig. update_traces(marker_color='rgb(182,229,159)')
fig.add_trace(go.Line(marker=dict(color='rgb(200,45, 35)'),
    x=utilisation['Year'], y=utilisation['Utilisation Rate'], name="Utilization Rate %", line={'dash': 'solid'}),
    secondary_y=True)
# fig. update_traces(marker_color='rgb(176,224, 230)')

fig.update_yaxes(title_text="<b>Annual Growth (millions m3)", secondary_y=False, range= [50,135] )
fig.update_xaxes( title_text="Year"
#                  ,  dtick= 'M60', tick0='1961'
                )
fig.update_yaxes(title_text="<b>Utilization Rate % *", secondary_y=True, range= [40,100])

fig.update_layout(height=500, width= 1000,legend=dict(
        x=1.1,
        y=0.8), paper_bgcolor='rgba(0,0,0,0)',
    plot_bgcolor='rgba(0,0,0,0)', title= '<b>Although the forest capacity varies, Utilisation is always on the high', title_x=0.4)

fig.write_image("pics/fig2.png")
fig.show()

C:\Users\mihai\AppData\Roaming\Python\Python39\site-packages\plotly\graph_objs\_deprecations.py:378: DeprecationWarning:

plotly.graph_objs.Line is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.scatter.Line
  - plotly.graph_objs.layout.shape.Line
  - etc.


import plotly.graph_objects as go
from plotly.subplots import make_subplots


fig = go.Figure()
fig = make_subplots(specs=[[{"secondary_y": True}]])

fig.add_trace(go.Bar(marker=dict(color='rgb(150,150,150)'),
                     x=utilisation['Year'], 
                     y= utilisation['Total fellings millions m3'], 
                     name="Annual Fellings (mil. m3)"),
             
                     secondary_y=False 
            
             )
# fig. update_traces(marker_color='rgb(182,229,159)')
fig.add_trace(go.Line(marker=dict(color='rgb(200,45, 35)'),
    x=utilisation['Year'], y=utilisation['Utilisation Rate'], name="Utilization Rate %", line={'dash': 'solid'}),
    secondary_y=True)
# fig. update_traces(marker_color='rgb(176,224, 230)')

fig.update_yaxes(title_text="<b>Annual Fellings (millions m3)", secondary_y=False, range= [50,100] )
fig.update_xaxes( title_text="Year"
#                  ,  dtick= 'M60', tick0='1961'
                )
fig.update_yaxes(title_text="<b>Utilization Rate % *", secondary_y=True, range= [50,100])

fig.update_layout(height=500, width= 1000,legend=dict(
        x=1.1,
        y=0.8), paper_bgcolor='rgba(0,0,0,0)',
    plot_bgcolor='rgba(0,0,0,0)', title= '<b>Although the wood supply varies, Utilisation rate is always on the high', title_x=0.4)


utilisation.head()


fig2 = go.Figure()

fig2.add_trace(go.Scatter(x= utilisation['Year'], y=utilisation['Total Annual Growth millions m3'], name= 'Total Annual Growth', fill='tozeroy', fillcolor= 'rgb(178,217,154)', mode= 'none', legendrank=1)) # fill to trace0 y
fig2.add_trace(go.Scatter(x= utilisation['Year'], y=utilisation['Total Annual Removals millions m3'], name= 'Total Annual Removals (including Natural causes)', fill='tozeroy', fillcolor= 'rgb(193, 170,5)', mode= 'none', legendrank=2)) # fill down to 

fig2.add_trace(go.Scatter(x= utilisation['Year'], y=utilisation['Natural decay millions m3'], name= 'Natural Decay', fill='tozeroy', fillcolor= 'rgb(193, 193,193)', mode= 'none', legendrank=3)) # fill down to 

fig2.update_layout(height=600, width= 1000,  
#                    legend=dict(
#         x=1.1,
#         y=0.9), 
#                    paper_bgcolor='rgba(0,0,0,0)',
#     plot_bgcolor='rgba(0,0,0,0)', 
                  title= '<b>Forest Growth versus Utilisation (wood volume)', title_x=0.1, title_y= 0.9,
                 yaxis_title="Million cubic metres", xaxis_title="Year")
fig2.add_annotation(x=2016, y=5,
            text="Natural Decay",
            showarrow=False,
                 font=dict(
#             family="Courier New, monospace",
            size=12,
            color="black"))


import chart_studio
import chart_studio.plotly as py
import chart_studio.tools as tls
import plotly.graph_objects as go
from chart_studio.grid_objs import Column, Grid

from datetime import datetime as dt


cumulative_clear.head()


import chart_studio
import chart_studio.plotly as py
import chart_studio.tools as tls
import plotly.graph_objects as go
from chart_studio.grid_objs import Column, Grid

import time
import numpy as np
from scipy.stats import multivariate_normal as Nd


import pandas as pd
test_heatmap= pd.read_csv(r'graphs\age structure test heatmap.csv')


test_heatmap.head()


N = len(test_heatmap['Year'].unique())
# heatmaps = [np.random.random((20, 20)) for i in range(N)]
N

15


heatmaps = [test_heatmap[test_heatmap['Year']==i].iloc[:, 4:] for i in test_heatmap['Year'].unique()]


type(heatmaps)

list


for i in heatmaps:
    print(i.info())

<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 0 to 20
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 21 to 41
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 42 to 62
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 63 to 83
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 84 to 104
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 105 to 125
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 126 to 146
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 147 to 167
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 168 to 188
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 189 to 209
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 210 to 230
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 231 to 251
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 252 to 272
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 273 to 293
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None
<class 'pandas.core.frame.DataFrame'>
Int64Index: 21 entries, 294 to 314
Data columns (total 11 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Clear-cut            21 non-null     float64
 1   3-10 years old       21 non-null     float64
 2   11 - 20 years old    21 non-null     float64
 3   21 - 30 years old    21 non-null     float64
 4   31 - 40 years old    21 non-null     float64
 5   41 - 60 years old    21 non-null     float64
 6   61 - 80 years old    21 non-null     float64
 7   81 - 100 years old   21 non-null     float64
 8   101 - 120 years old  21 non-null     float64
 9   121 - 140 years old  21 non-null     float64
 10  141+ years old       21 non-null     float64
dtypes: float64(11)
memory usage: 2.0 KB
None


import plotly.graph_objects as go
import numpy as np

fig = go.Figure(data=go.Heatmap(z=heatmaps[0]),
               frames=[go.Frame(data=go.Heatmap(z=heatmaps[i])) for i in range(N)])
fig.update_layout(
        updatemenus=[
        dict(type="buttons", visible=True,
        buttons=[dict(label="Play", method="animate", args=[None])]
            )])
fig.show()


import pandas as pd
test_heatmap= pd.read_csv(r'graphs\age structure test heatmap.csv')


age_structure_protected_versus_unprot = pd.read_csv(r'graphs\age structure protected versus unprotected all country.csv')


age_structure_protected_versus_unprot.head()


age_structure_protected_versus_unprot.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 30 entries, 0 to 29
Data columns (total 14 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Year                      30 non-null     int64  
 1   Average age               30 non-null     float64
 2   Type of Forest            30 non-null     object 
 3   Clear-cut Percent         30 non-null     float64
 4   3-10 years  Percent       30 non-null     float64
 5   11 - 20 years  Percent    30 non-null     float64
 6   21 - 30 years  Percent    30 non-null     float64
 7   31 - 40 years  Percent    30 non-null     float64
 8   41 - 60 years  Percent    30 non-null     float64
 9   61 - 80 years  Percent    30 non-null     float64
 10  81 - 100 years  Percent   30 non-null     float64
 11  101 - 120 years  Percent  30 non-null     float64
 12  121 - 140 years  Percent  30 non-null     float64
 13  141+ years  Percent       30 non-null     float64
dtypes: float64(12), int64(1), object(1)
memory usage: 3.4+ KB


bar_animated_prot_unprot2=  pd.melt(age_structure_protected_versus_unprot, id_vars= ['Year', 'Type of Forest', 'Average age'], value_vars= age_structure_protected_versus_unprot.columns[3:], var_name= 'Age Category Percent', value_name= 'Percent of Surface' )


bar_animated_prot_unprot2.head()


bar_animated_prot_unprot2['Age Category Percent'].value_counts()

Clear-cut Percent           30
3-10 years  Percent         30
11 - 20 years  Percent      30
21 - 30 years  Percent      30
31 - 40 years  Percent      30
41 - 60 years  Percent      30
61 - 80 years  Percent      30
81 - 100 years  Percent     30
101 - 120 years  Percent    30
121 - 140 years  Percent    30
141+ years  Percent         30
Name: Age Category Percent, dtype: int64


bar_animated_prot_unprot2


bar_animated=  pd.melt(test_heatmap, id_vars= ['Year', 'Region'], value_vars= test_heatmap.columns[4:], var_name= 'Age Category', value_name= 'Surface (1000 hectares)' )


# melt all categories under 'Age category', and groupby Region


bar_animated=  pd.melt(test_heatmap, id_vars= ['Year', 'Region'], value_vars= test_heatmap.columns[4:], var_name= 'Age Category', value_name= 'Surface (1000 hectares)' )


bar_animated


bar_animated_prot_unprot2


import plotly.express as px
# for i in bar_animated_prot_unprot2['County'].unique():
# bar_animated_prot_unprot2_data= bar_animated_prot_unprot2[bar_animated_prot_unprot2['County']==i]

fig = px.bar(bar_animated_prot_unprot2, x='Age Category Percent', y='Percent of Surface', 
                 color='Type of Forest', 
             color_discrete_map={'Unprotected': 'rgb(188,188,188)',
                                    'Protected': 'rgb(29,121,29)'},
                 animation_frame='Year', 
                 range_y=[0,60], barmode="group")
fig.update_layout(
        width=800,
    height=500,
    title={
        'text': '<b>Comparison of Age Structure - Production versus Natural Forests',
        'y':0.95,
        'x':0.45,
        'xanchor': 'center',
        'yanchor': 'top'})
fig.show()


bar_animated_region_prot= pd.read_csv(r'graphs\age structure protected versus unprotected region.csv')


bar_animated_region_prot.head()


bar_animated_region_prot= bar_animated_region_prot.iloc[:, :-1]


bar_animated_region=  pd.melt(bar_animated_region_prot, id_vars= ['Year', 'Region', 'Type of Forest'], value_vars= bar_animated_region_prot.columns[3:], var_name= 'Age Category Percent', value_name= 'Percent of Surface' )


bar_animated_region


import plotly.express as px
for i in bar_animated_region['Region'].unique():
    bar_animated_region_data = bar_animated_region[bar_animated_region['Region']==i]

    fig = px.bar(bar_animated_region_data, x='Age Category Percent', y='Percent of Surface', 
                 color='Type of Forest',
                 facet_col='Type of Forest',
#                                   color_discrete_sequence=px.colors.sequential.Greens,
                 color_discrete_map={'Unprotected': 'rgb(188,188,188)',
                                    'Protected': 'rgb(29,121,29)'},
                 animation_frame='Year', 
                 range_y=[0,55], barmode="group")
    fig.update_layout(
        width=1000,
    height=550,
        legend=dict(x=0,y=-0.7),
    title={
        'text': '<b>Protected versus Exploited Forest: Age Structure - ' + str(i),
        'y':0.98,
        'x':0.45,
        'xanchor': 'center',
        'yanchor': 'top'})
    fig.show()


import numpy as np
bar_animated_pivot= pd.pivot_table(bar_animated, index= ['Year', 'Region', 'Age Category'],  values= 'Surface (1000 hectares)', aggfunc= np.sum)


bar_animated_pivot= bar_animated_pivot.reset_index()


# Arrange the age structure by logic
age_categories= bar_animated_pivot['Age Category'].unique()
order= [9, 3, 10, 11, 4, 2, 5,6,7, 8,1]


age= pd.DataFrame()


age['Age Category']= age_categories
age['Order']= order

age


bar_animated_pivot= bar_animated_pivot.merge(age, on= 'Age Category', how='outer')


bar_animated_pivot= bar_animated_pivot.sort_values( by= ['Region', 'Year', 'Order'])


bar_animated_pivot


bar_animated_pivot.head()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_1400/3022137967.py in <module>
----> 1 bar_animated_pivot.head()

NameError: name 'bar_animated_pivot' is not defined


import plotly.express as px
values = list(range(10))

for i in bar_animated_pivot['Region'].unique():
    bar_animated_data= bar_animated_pivot[bar_animated_pivot['Region']==i]

    fig = px.bar(bar_animated_data, x='Age Category', y='Surface (1000 hectares)', 
#                  color='Age Category', 
                 animation_frame='Year', 
                 range_y=[0,1300],
                color="Age Category", 
#                  color_discrete_sequence=px.colors.sequential.Greens
                 color_discrete_map={'Clear-cut': 'rgb(227,239,227)', 
                                     '3-10 years old': 'rgb(200,238,200)', 
                                     '11 - 20 years old': 'rgb(180,238,180)',
                                     '21 - 30 years old': 'rgb(141,209,141)',
                                     '31 - 40 years old': 'rgb(98,188,98)',
                                     '41 - 60 years old': 'rgb(74,171,74)',
                                     '61 - 80 years old': 'rgb(47,146,47)', 
                                     '81 - 100 years old': 'rgb(29,121,29)', 
                                     '101 - 120 years old': 'rgb(17,97,17)',
                                     '121 - 140 years old': 'rgb(10,77,10)', 
                                     '141+ years old': 'rgb(3,57,3)'
                                    
                                    
                                    
                                    })
               
    fig.update_layout(
        width=800,
    height=500,
    title={
        'text': "<b>Change in Age Structure by Region (All Forest)- " + str(i),
        'y':0.95,
        'x':0.45,
        'xanchor': 'center',
        'yanchor': 'top'})
  
    
    fig.show()


redlisted_category_2022= pd.read_csv(r'graphs\redlisted by category 2022.csv')


redlisted_category_2022.head()


redlisted_category_2022 = redlisted_category_2022[['Taxon id', 'Lives in the forest', 'Forest is essential', 'RedListCategory', 'Category']]


redlisted_category_2022.head()


redlisted_category_2022_pivot= pd.pivot_table(redlisted_category_2022, index= 'Category', columns= [ 'RedListCategory'], values= 'Taxon id', aggfunc= 'count', margins= True)


redlisted_category_2022_pivot= redlisted_category_2022_pivot.fillna(0)


redlisted_category_2022_pivot= redlisted_category_2022_pivot.reset_index()


redlisted_category_2022_pivot= redlisted_category_2022_pivot[:-1]


redlisted_category_2022_pivot


redlisted_category_2022_pivot= redlisted_category_2022_pivot.sort_values(by= 'All', ascending= False)


redlisted_category_2022_pivot= redlisted_category_2022_pivot[['Category','Data Defficient', 'Near Threatened',  'Vulnerable', 'Endangered', 'Critically Endangered',  'Regionally Extinct'  ]]


sum_category= redlisted_category_2022_pivot.groupby('Category').sum()


# redlisted_category_2022_pivot=redlisted_category_2022_pivot.reset_index()


redlisted_category_2022_pivot= pd.melt(redlisted_category_2022_pivot, id_vars= ['Category'], value_vars= redlisted_category_2022_pivot.columns[1:7], var_name= 'Redlist Category', value_name= 'Number of Species' )


# add a number to the bar on top


import numpy as np
sum_category= pd.pivot_table(redlisted_category_2022_pivot, index= 'Category', values= 'Number of Species', aggfunc= np.sum)


sum_category


redlisted_category_2022_pivot= redlisted_category_2022_pivot.merge(sum_category, on= 'Category', how= 'outer')


redlisted_category_2022_pivot= redlisted_category_2022_pivot.rename(columns= {'Number of Species_x': 'Number of Species', 'Number of Species_y': 'Sum Number of Species'})


redlisted_category_2022_pivot.iloc[0:5, 3]=np.nan
redlisted_category_2022_pivot.iloc[6:11, 3]=np.nan
redlisted_category_2022_pivot.iloc[12:17, 3]=np.nan
redlisted_category_2022_pivot.iloc[18:23, 3]=np.nan
redlisted_category_2022_pivot.iloc[24:29, 3]=np.nan
redlisted_category_2022_pivot.iloc[30:35, 3]=np.nan
redlisted_category_2022_pivot.iloc[36:41, 3]=np.nan
redlisted_category_2022_pivot.iloc[42:47, 3]=np.nan


redlisted_category_2022_pivot.iloc[29, 2]=0.001
redlisted_category_2022_pivot.iloc[35, 2]=0.001
redlisted_category_2022_pivot.iloc[47, 2]=0.001


redlisted_category_2022_pivot


redlisted_category_2022_pivot.iloc[46, 3]=np.nan
redlisted_category_2022_pivot.iloc[34, 3]=np.nan
redlisted_category_2022_pivot.iloc[28, 3]=np.nan


import plotly.express as px
fig = go.Figure()

fig = px.bar(redlisted_category_2022_pivot, x='Category', y='Number of Species', 
                 color='Redlist Category', 
#              text= 'Number of Species',
#              barmode= 'overlay',
             color_discrete_sequence=px.colors.sequential.Reds[3:],
#             color_discrete_map={'Data Deficient': 'rgb(220,220,220)', 
#                                      'Near Threatened': 'rgb(236,192,185)'}, 
#                                 
            range_y= [0,1200], text= 'Sum Number of Species')
   

fig.update_layout(uniformtext=dict(minsize=10, mode='show'))
fig.update_traces(textangle=0, textposition="outside", cliponaxis=False)

fig.update_layout(
        width=800,
    height=500,
    legend=dict(x=1,y=0.8),
     plot_bgcolor='rgba(0,0,0,0)',
    title={
        'text': "<b>Endangered Forest-Living Species by Status - 2022" ,
        'y':0.95,
        'x':0.45,
        'xanchor': 'center',
        'yanchor': 'top'})
fig.update_xaxes(title_font_family="Times New Roman")


fig.show()


redlisted_2022_2015= pd.read_csv(r'graphs\\REDLISTED\2022 versus 2015 - by main category.csv')


redlisted_2022_2015


redlisted_2022_2015= redlisted_2022_2015.sort_values(by= '2022', ascending= False)


redlisted_2022_2015.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 10 entries, 0 to 2
Data columns (total 3 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Category  10 non-null     object 
 1   2022      10 non-null     int64  
 2   2015      9 non-null      float64
dtypes: float64(1), int64(1), object(1)
memory usage: 320.0+ bytes


redlisted_2022_2015= redlisted_2022_2015[redlisted_2022_2015['Category']!= 'All redlisted species']


redlisted_2022_2015


fig = go.Figure()
fig = make_subplots(specs=[[{"secondary_y": True}]])

fig.add_trace(go.Bar(marker=dict(color='rgb(218,212,220)'),
                     x=redlisted_2022_2015['Category'], 
                     y= redlisted_2022_2015['2015'], 
                     name="2015 Redlisted Species"),
             
                     secondary_y=False 
            
             )


fig.add_trace(go.Bar(marker=dict(color='rgb(162,108,165)'),
                     x=redlisted_2022_2015['Category'], 
                     y= redlisted_2022_2015['2022'], 
                     name="2022 Redlisted Species"),
             
                     secondary_y=False 
            
             )


# fig. update_traces(marker_color='rgb(182,229,159)')
fig.add_trace(go.Scatter(mode='markers', marker=dict(color='rgb(200,45, 35)', size=12, symbol= 'x'),
    x=redlisted_2022_2015['Category'], y=redlisted_2022_2015['Percent'], name="% of all Forest Species in this Category"
#                          , 
#                          line={'dash': 'dash'}
                        )
                              ,
    secondary_y=True)
# fig. update_traces(marker_color='rgb(176,224, 230)')

fig.update_yaxes(title_text="<b>Number of Redlisted Species", secondary_y=False, range= [0,1000] )
fig.update_xaxes( title_text="Category"
#                  ,  dtick= 'M60', tick0='1961'
                )
fig.update_yaxes(title_text='<b>% of all Forest Living Species', secondary_y=True, range= [0,70])

fig.update_layout(height=500, width= 1000,legend=dict(
        x=1.1,
        y=0.8), paper_bgcolor='rgba(0,0,0,0)',
    plot_bgcolor='rgba(0,0,0,0)', title= '<b>Increasing Number of Redlisted Forest Species', title_x=0.4)

fig.write_image("pics/fig4.png")
fig.show()


redlisted_cat= pd.read_csv(r'graphs\REDLISTED\2022 versus 2015 - by main category no total.csv')


redlisted_cat


from plotly import data
df = data.gapminder()


redlisted_cat.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18 entries, 0 to 17
Data columns (total 3 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   Category           18 non-null     object 
 1   Year               18 non-null     int64  
 2   Number of Species  17 non-null     float64
dtypes: float64(1), int64(1), object(1)
memory usage: 560.0+ bytes


df = redlisted_cat


categories = redlisted_cat['Category'].unique()


data = {"x": [], "y": [], "colors": [], "years": []}

for cat in categories:
    data["x"].extend(
        [
            redlisted_cat.loc[(redlisted_cat.Year == 2015) & (redlisted_cat.Category == cat)]["Number of Species"].values[0],
            redlisted_cat.loc[(redlisted_cat.Year == 2022) & (redlisted_cat.Category == cat)]["Number of Species"].values[0],
            None,
        ]
    )
    data["y"].extend([cat, cat, None]),
    data["colors"].extend(["silver", "lightskyblue", "white"]),
    data["years"].extend(["2015", "2020", None])

fig = go.Figure(
    data=[
        go.Scatter(
            x=data["x"],
            y=data["y"],
            mode="markers+lines",
            marker=dict(
                symbol="arrow-right", color="black", size=10, 
#                 angleref="previous", 
#                 standoff=8
            ),
        ),
        go.Scatter(
            x=data["x"],
            y=data["y"],
            text=data["years"],
            mode="markers",
            marker=dict(
                color=data["colors"],
                size=16,
            ),
            hovertemplate="""Country: %{y} <br> Life Expectancy: %{x} <br> Year: %{text} <br><extra></extra>""",
        ),
    ]
)

fig.update_layout(
    title="Life Expectancy in Europe: 1952 and 2002",
    width=800,
    height=700,
    showlegend=False,
)


fig.show()


redlisted_cat.loc[(redlisted_cat.Year == 2015) & (redlisted_cat.Category == 'Lichens')]


redlisted_category_2022_pivot= redlisted_category_2022_pivot[redlisted_category_2022_pivot['Redlist Category']!= 'All']


redlisted_category_2022_pivot= redlisted_category_2022_pivot[redlisted_category_2022_pivot['Category']!= 'All']


redlisted_category_2022_pivot['Redlist Category'].unique()

array(['Regionally Extinct', 'Critically Endangered', 'Endangered',
       'Vulnerable', 'Near Threatened', 'Data Defficient'], dtype=object)


redlisted_category_2022_pivot['Redlist Category']= redlisted_category_2022_pivot['Redlist Category'].str.replace('CR', 'Critically Endangered')
redlisted_category_2022_pivot['Redlist Category']= redlisted_category_2022_pivot['Redlist Category'].str.replace('DD', 'Data Deficient')
redlisted_category_2022_pivot['Redlist Category']= redlisted_category_2022_pivot['Redlist Category'].str.replace('EN', 'Endangered')
redlisted_category_2022_pivot['Redlist Category']= redlisted_category_2022_pivot['Redlist Category'].str.replace('NT', 'Near Threatened')
redlisted_category_2022_pivot['Redlist Category']= redlisted_category_2022_pivot['Redlist Category'].str.replace('RE', 'Regionally Extinct')
redlisted_category_2022_pivot['Redlist Category']= redlisted_category_2022_pivot['Redlist Category'].str.replace('VU', 'Vulnerable')


# format the numbers to show no decimal

redlisted_category_2022_pivot["Number of Species"]= redlisted_category_2022_pivot["Number of Species"].round().astype(int)


len(fig.data[0].labels)

56


fig.data[0].labels[48:56]

array(['Birds', 'Insects', 'Lichens', 'Mammals', 'Mushrooms',
       'Nonvertebrates', 'Other vertebrates', 'Vascular plants'],
      dtype=object)


fig.data[0].values[48:56]

array([  52, 1037,  250,   20,  753,   18,    3,  690])


fig.data[0]

Sunburst({
    'branchvalues': 'total',
    'customdata': array([['Mammals'],
                         ['Birds'],
                         ['Insects'],
                         ['Lichens'],
                         ['Mushrooms'],
                         ['Nonvertebrates'],
                         ['Birds'],
                         ['Mushrooms'],
                         ['Mammals'],
                         ['Lichens'],
                         ['Insects'],
                         ['Nonvertebrates'],
                         ['Mammals'],
                         ['Other vertebrates'],
                         ['Nonvertebrates'],
                         ['Mushrooms'],
                         ['Lichens'],
                         ['Vascular plants'],
                         ['Birds'],
                         ['Mammals'],
                         ['Lichens'],
                         ['Insects'],
                         ['Vascular plants'],
                         ['Other vertebrates'],
                         ['Other vertebrates'],
                         ['Birds'],
                         ['Vascular plants'],
                         ['Lichens'],
                         ['Mammals'],
                         ['Insects'],
                         ['Other vertebrates'],
                         ['Insects'],
                         ['Birds'],
                         ['Mushrooms'],
                         ['Nonvertebrates'],
                         ['Vascular plants'],
                         ['Lichens'],
                         ['Mammals'],
                         ['Mushrooms'],
                         ['Nonvertebrates'],
                         ['Vascular plants'],
                         ['Other vertebrates'],
                         ['Mushrooms'],
                         ['Nonvertebrates'],
                         ['Other vertebrates'],
                         ['Vascular plants'],
                         ['Insects'],
                         ['Birds'],
                         ['Birds'],
                         ['Insects'],
                         ['Lichens'],
                         ['Mammals'],
                         ['Mushrooms'],
                         ['Nonvertebrates'],
                         ['Other vertebrates'],
                         ['Vascular plants']], dtype=object),
    'domain': {'x': [0.0, 1.0], 'y': [0.0, 1.0]},
    'hovertemplate': ('labels=%{label}<br>Number of S' ... '{customdata[0]}<extra></extra>'),
    'ids': array(['Mammals/Near Threatened', 'Birds/Near Threatened',
                  'Insects/Near Threatened', 'Lichens/Near Threatened',
                  'Mushrooms/Near Threatened', 'Nonvertebrates/Near Threatened',
                  'Birds/Critically Endangered', 'Mushrooms/Critically Endangered',
                  'Mammals/Critically Endangered', 'Lichens/Critically Endangered',
                  'Insects/Critically Endangered', 'Nonvertebrates/Critically Endangered',
                  'Mammals/Endangered', 'Other vertebrates/Endangered',
                  'Nonvertebrates/Endangered', 'Mushrooms/Endangered',
                  'Lichens/Endangered', 'Vascular plants/Endangered',
                  'Birds/Regionally Extinct', 'Mammals/Regionally Extinct',
                  'Lichens/Regionally Extinct', 'Insects/Regionally Extinct',
                  'Vascular plants/Near Threatened', 'Other vertebrates/Near Threatened',
                  'Other vertebrates/Critically Endangered', 'Birds/Data Defficient',
                  'Vascular plants/Critically Endangered', 'Lichens/Data Defficient',
                  'Mammals/Data Defficient', 'Insects/Data Defficient',
                  'Other vertebrates/Regionally Extinct', 'Insects/Vulnerable',
                  'Birds/Vulnerable', 'Mushrooms/Regionally Extinct',
                  'Nonvertebrates/Regionally Extinct',
                  'Vascular plants/Regionally Extinct', 'Lichens/Vulnerable',
                  'Mammals/Vulnerable', 'Mushrooms/Vulnerable',
                  'Nonvertebrates/Vulnerable', 'Vascular plants/Vulnerable',
                  'Other vertebrates/Vulnerable', 'Mushrooms/Data Defficient',
                  'Nonvertebrates/Data Defficient', 'Other vertebrates/Data Defficient',
                  'Vascular plants/Data Defficient', 'Insects/Endangered',
                  'Birds/Endangered', 'Birds', 'Insects', 'Lichens', 'Mammals',
                  'Mushrooms', 'Nonvertebrates', 'Other vertebrates', 'Vascular plants'],
                 dtype=object),
    'labels': array(['Near Threatened', 'Near Threatened', 'Near Threatened',
                     'Near Threatened', 'Near Threatened', 'Near Threatened',
                     'Critically Endangered', 'Critically Endangered',
                     'Critically Endangered', 'Critically Endangered',
                     'Critically Endangered', 'Critically Endangered', 'Endangered',
                     'Endangered', 'Endangered', 'Endangered', 'Endangered', 'Endangered',
                     'Regionally Extinct', 'Regionally Extinct', 'Regionally Extinct',
                     'Regionally Extinct', 'Near Threatened', 'Near Threatened',
                     'Critically Endangered', 'Data Defficient', 'Critically Endangered',
                     'Data Defficient', 'Data Defficient', 'Data Defficient',
                     'Regionally Extinct', 'Vulnerable', 'Vulnerable', 'Regionally Extinct',
                     'Regionally Extinct', 'Regionally Extinct', 'Vulnerable', 'Vulnerable',
                     'Vulnerable', 'Vulnerable', 'Vulnerable', 'Vulnerable',
                     'Data Defficient', 'Data Defficient', 'Data Defficient',
                     'Data Defficient', 'Endangered', 'Endangered', 'Birds', 'Insects',
                     'Lichens', 'Mammals', 'Mushrooms', 'Nonvertebrates',
                     'Other vertebrates', 'Vascular plants'], dtype=object),
    'marker': {'colors': [rgb(103,0,31), rgb(178,24,43), rgb(214,96,77),
                          rgb(244,165,130), rgb(253,219,199), rgb(247,247,247),
                          rgb(178,24,43), rgb(253,219,199), rgb(103,0,31),
                          rgb(244,165,130), rgb(214,96,77), rgb(247,247,247),
                          rgb(103,0,31), rgb(209,229,240), rgb(247,247,247),
                          rgb(253,219,199), rgb(244,165,130), rgb(146,197,222),
                          rgb(178,24,43), rgb(103,0,31), rgb(244,165,130),
                          rgb(214,96,77), rgb(146,197,222), rgb(209,229,240),
                          rgb(209,229,240), rgb(178,24,43), rgb(146,197,222),
                          rgb(244,165,130), rgb(103,0,31), rgb(214,96,77),
                          rgb(209,229,240), rgb(214,96,77), rgb(178,24,43),
                          rgb(253,219,199), rgb(247,247,247), rgb(146,197,222),
                          rgb(244,165,130), rgb(103,0,31), rgb(253,219,199),
                          rgb(247,247,247), rgb(146,197,222), rgb(209,229,240),
                          rgb(253,219,199), rgb(247,247,247), rgb(209,229,240),
                          rgb(146,197,222), rgb(214,96,77), rgb(178,24,43),
                          rgb(178,24,43), rgb(214,96,77), rgb(244,165,130),
                          rgb(103,0,31), rgb(253,219,199), rgb(247,247,247),
                          rgb(209,229,240), rgb(146,197,222)]},
    'name': '',
    'parents': array(['Mammals', 'Birds', 'Insects', 'Lichens', 'Mushrooms', 'Nonvertebrates',
                      'Birds', 'Mushrooms', 'Mammals', 'Lichens', 'Insects', 'Nonvertebrates',
                      'Mammals', 'Other vertebrates', 'Nonvertebrates', 'Mushrooms',
                      'Lichens', 'Vascular plants', 'Birds', 'Mammals', 'Lichens', 'Insects',
                      'Vascular plants', 'Other vertebrates', 'Other vertebrates', 'Birds',
                      'Vascular plants', 'Lichens', 'Mammals', 'Insects', 'Other vertebrates',
                      'Insects', 'Birds', 'Mushrooms', 'Nonvertebrates', 'Vascular plants',
                      'Lichens', 'Mammals', 'Mushrooms', 'Nonvertebrates', 'Vascular plants',
                      'Other vertebrates', 'Mushrooms', 'Nonvertebrates', 'Other vertebrates',
                      'Vascular plants', 'Insects', 'Birds', '', '', '', '', '', '', '', ''],
                     dtype=object),
    'values': array([  10,   31,  466,   57,  238,    9,    4,   14,    1,   43,   23,    0,
                        4,    0,    1,   96,   54,  168,    4,    1,    8,   13,  125,    1,
                        0,    0,   39,   18,    0,  128,    0,  280,    7,    2,    0,    3,
                       70,    4,  276,    7,  182,    2,  127,    1,    0,  173,  127,    6,
                       52, 1037,  250,   20,  753,   18,    3,  690])
})


fig = px.sunburst(redlisted_category_2022_pivot, path=['Category', 'Redlist Category'], values='Number of Species', 
                  color='Category', 
#                   color_discrete_map={ 'Critically Endangered':'rgb(255, 0,0)', 
#                                       'Endangered':'rgb(249, 238, 141)', 
#                                       'Vulnerable': 'rgb(235, 249, 141)', 
#                                       'Data Deficient': 'rgb(240, 240,240)',
#                                       'Regionally Extinct': 'rgb(40,40,40)',
#                                       'Near Threatened': 'rgb(212, 218, 179)'}
                                     
                        color_discrete_sequence=px.colors.sequential.RdBu  
            
                                     
                 )


fig.update_layout(height=800, width= 950
                  ,legend=dict(x=1,y=1.01), paper_bgcolor='rgba(0,0,0,0)',
    plot_bgcolor='rgba(0,0,0,0)', title= '<b>Number of Endangered Species Living in the Forest - 2022', title_x=0.5)

fig.update_layout(uniformtext=dict(minsize=12, mode='hide'
#                                    , face= 'bold'
                                  ))


fig.data[0].labels=np.array(list(zip(fig.data[0].labels,  fig.data[0].values)))
# fig.data[0].labels[48:56]=np.array(list(zip(fig.data[0].labels[48:56],  fig.data[0].values[48:56])))
# fig.update_traces(textinfo='text', selector=dict(type='sunburst'))
# fig.update_traces(level='Mushrooms', selector=dict(type='sunburst'))

fig.add_annotation(x=1.05, y=0.49,
            text="Non-vertebrates, 18",
            showarrow=False)
fig.add_annotation(x=1.04, y=0.46,
            text="Mammals, 20",
            showarrow=False)
fig.add_annotation(x=1, y=0.4,
            text="Birds, 52",
            showarrow=False)

fig.show()

C:\Users\mihai\AppData\Roaming\Python\Python39\site-packages\plotly\express\_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

C:\Users\mihai\AppData\Roaming\Python\Python39\site-packages\plotly\express\_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.


main_labels= np.array(list(zip(fig.data[0].labels[48:56],  fig.data[0].values[48:56])))


main_labels[0]

array(['Birds', '52'], dtype='<U17')


env= pd.read_csv(r'graphs\Redlisted by forest type.csv')


env.head()


import plotly.graph_objects as go
from plotly.subplots import make_subplots


fig = go.Figure()

fig = px.bar(env, x='Living environment', y='Number of species', 
                 color='Measure', barmode= 'overlay',
             color_discrete_sequence=px.colors.sequential.Hot,
#             color_discrete_map={'Data Deficient': 'rgb(220,220,220)', 
#                                      'Near Threatened': 'rgb(236,192,185)'}, 
#                                 
            range_y= [0,17000])
            

#        
fig.update_layout(
        width=1000,
    height=600,
#      plot_bgcolor='rgba(0,0,0,0)',
    title={
        'text': "<b>Redlisted Species by Living Environment" ,
        'y':0.95,
        'x':0.4,
        'xanchor': 'center',
        'yanchor': 'top'})

# fig.add_annotation(x=0, y=2000,
#             text="Natural Forest",
#             showarrow=False,
#                  font=dict(
# #             family="Courier New, monospace",
#             size=16))

fig.update_layout(xaxis_title="Environment")
    
fig.show()


# take out Total number of species living there
# introduce the year? 


import plotly.graph_objects as go
from plotly.subplots import make_subplots


fig = go.Figure()

fig = px.bar(env, x='Living environment', y='Number of species', 
                 color='Measure', barmode= 'overlay',
             color_discrete_sequence=px.colors.sequential.Hot,
#             color_discrete_map={'Data Deficient': 'rgb(220,220,220)', 
#                                      'Near Threatened': 'rgb(236,192,185)'}, 
#                                 
            range_y= [0,17000])
            

#        
fig.update_layout(
        width=900,
    height=500,
#      plot_bgcolor='rgba(0,0,0,0)',
    title={
        'text': "<b>Forest Living Species - Redlisted 2022" ,
        'y':0.95,
        'x':0.4,
        'xanchor': 'center',
        'yanchor': 'top'})

# fig.add_annotation(x=0, y=2000,
#             text="Natural Forest",
#             showarrow=False,
#                  font=dict(
# #             family="Courier New, monospace",
#             size=16))

fig.update_layout(xaxis_title="Environment")
    
fig.show()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_2632/2823793312.py in <module>
      9 fig = go.Figure()
     10 
---> 11 fig = px.bar(env, x='Living environment', y='Number of species', 
     12                  color='Measure', barmode= 'overlay',
     13              color_discrete_sequence=px.colors.sequential.Hot,

NameError: name 'env' is not defined


px.colors.sequential.swatches(template=None)


env.head()


polar_env= pd.read_csv(r'graphs\Redlisted by forest type_polar chart.csv')


polar_env= polar_env.rename(columns= {'Unnamed: 0': 'Environment' })


fig = go.Figure(go.Barpolar(
    r= polar_env['Total number of species living here']/3000,
    theta=[65, 15, 210, 110, 312.5],
    width=[20,15,10,20,15],
    marker_color=['#709BFF', '#FFAA70', '#FFAA70', '#FFDF70', '#B6FFB4'],
    marker_line_color="black",
    marker_line_width=2,
    opacity=0.8
))

fig.update_layout(
    template=None,
    polar = dict(
        radialaxis = dict(range=[0, 5], showticklabels=False, ticks=''),
        angularaxis = dict(showticklabels=False, ticks='')
    )
)

fig.show()


forest_redlisted_factors= pd.read_csv(r'graphs\redlisted skogslevande negative factors.csv')


forest_redlisted_factors


forest_redlisted_factors_piv= pd.pivot_table(forest_redlisted_factors, index= 'RedListCategory EXPLAINED', columns='Factor for Negative Outlook', values='species id', aggfunc= 'count', margins= True)


forest_redlisted_factors_piv= forest_redlisted_factors_piv.reset_index()


forest_redlisted_factors_piv= forest_redlisted_factors_piv[['RedListCategory EXPLAINED', 'Clear-cutting', 'Forest overgrowth on other land types', 'Forest replanting', 'Forest ditching', 'Fire prevention', 'Soil Acidification', 'Water systems damage', 'Pesticides', 'Climate change']]


forest_redlisted_factors_piv= forest_redlisted_factors_piv[:-1]


forest_redlisted_factors_piv= forest_redlisted_factors_piv.fillna(0)


forest_redlisted_factors_piv.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5
Data columns (total 10 columns):
 #   Column                                 Non-Null Count  Dtype  
---  ------                                 --------------  -----  
 0   RedListCategory EXPLAINED              6 non-null      object 
 1   Clear-cutting                          6 non-null      float64
 2   Forest overgrowth on other land types  6 non-null      float64
 3   Forest replanting                      6 non-null      float64
 4   Forest ditching                        6 non-null      float64
 5   Fire prevention                        6 non-null      float64
 6   Soil Acidification                     6 non-null      float64
 7   Water systems damage                   6 non-null      float64
 8   Pesticides                             6 non-null      float64
 9   Climate change                         6 non-null      float64
dtypes: float64(9), object(1)
memory usage: 608.0+ bytes


forest_redlisted_factors_piv['order']= [4, 0, 2, 1, 5, 3]


forest_redlisted_factors_piv.head()


forest_redlisted_factors_piv= forest_redlisted_factors_piv.sort_values(by='order')


forest_redlisted_factors_piv=  pd.melt(forest_redlisted_factors_piv, id_vars= ['RedListCategory EXPLAINED'], 
                                       value_vars= forest_redlisted_factors_piv.columns[1:10], 
                                       var_name= 'Factor for Negative Dev.', value_name= 'Number of Species Affected' )


forest_redlisted_factors_piv= forest_redlisted_factors_piv.rename(columns= {'RedListCategory EXPLAINED': 'Red List Category'})
forest_redlisted_factors_piv= forest_redlisted_factors_piv.rename(columns= {'Factor for Negative Dev.': 'Factor for Negative Development'})


forest_redlisted_factors_piv.head()


import plotly.graph_objects as go
from plotly.subplots import make_subplots


fig = go.Figure()

fig = px.bar(forest_redlisted_factors_piv, x='Factor for Negative Development', y='Number of Species Affected', 
                 color='Red List Category',
             color_discrete_sequence=px.colors.sequential.thermal_r,
#             color_discrete_map={'Data Deficient': 'rgb(220,220,220)', 
#                                      'Near Threatened': 'rgb(236,192,185)'}, 
#                                 
            range_y= [0,1200])

#        
fig.update_layout(
        width=1000,
    height=600,
#      plot_bgcolor='rgba(0,0,0,0)',
    title={
        'text': "<b>Forestry Practices Directly Affecting Redlisted Species" ,
        'y':0.95,
        'x':0.42,
        'xanchor': 'center',
        'yanchor': 'top'})

# fig.add_annotation(x=0, y=2000,
#             text="Natural Forest",
#             showarrow=False,
#                  font=dict(
# #             family="Courier New, monospace",
#             size=16))

fig.update_layout(xaxis_title="Factor for Negative Development*")
    
fig.show()


bar_animated_pivot.head()


help(px.colors.sequential)

Help on module _plotly_utils.colors.sequential in _plotly_utils.colors:

NAME
    _plotly_utils.colors.sequential - Sequential color scales are appropriate for most continuous data, but in some cases it can be helpful to use a `plotly.colors.diverging` or `plotly.colors.cyclical` scale instead. The color scales in this module are mostly meant to be passed in as the `color_continuous_scale` argument to various functions.

FUNCTIONS
    swatches(template=None)
        Parameters
        ----------
        template : str or dict or plotly.graph_objects.layout.Template instance
            The figure template name or definition.
        
        Returns
        -------
        fig : graph_objects.Figure containing the displayed image
            A `Figure` object. This figure demonstrates the color scales and
            sequences in this module, as stacked bar charts.

DATA
    __all__ = ['swatches']

FILE
    c:\users\mihai\appdata\roaming\python\python39\site-packages\_plotly_utils\colors\sequential.py


['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',
             'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg',
             'brwnyl', 'bugn', 'bupu', 'burg', 'burgyl', 'cividis', 'curl',
             'darkmint', 'deep', 'delta', 'dense', 'earth', 'edge', 'electric',
             'emrld', 'fall', 'geyser', 'gnbu', 'gray', 'greens', 'greys',
             'haline', 'hot', 'hsv', 'ice', 'icefire', 'inferno', 'jet',
             'magenta', 'magma', 'matter', 'mint', 'mrybm', 'mygbm', 'oranges',
             'orrd', 'oryel', 'oxy', 'peach', 'phase', 'picnic', 'pinkyl',
             'piyg', 'plasma', 'plotly3', 'portland', 'prgn', 'pubu', 'pubugn',
             'puor', 'purd', 'purp', 'purples', 'purpor', 'rainbow', 'rdbu',
             'rdgy', 'rdpu', 'rdylbu', 'rdylgn', 'redor', 'reds', 'solar',
             'spectral', 'speed', 'sunset', 'sunsetdark', 'teal', 'tealgrn',
             'tealrose', 'tempo', 'temps', 'thermal', 'tropic', 'turbid',
             'turbo', 'twilight', 'viridis', 'ylgn', 'ylgnbu', 'ylorbr',
             'ylorrd'].
        Appending '_r' to a named colorscale reverses it.


df = px.data.wind()

df

	County	All Land Areal	Protected areal i hektar 2021	Protected Landareal i hektar 2021	protected Water areal	Total Forest Areal per County	Total Productive Forest Area	Total Improductive Forest Area	Total productive forest inside nature protected areas	NOT PROTECTED Productive forest inside nature protected areas	PROTECTED Productive forest inside formally protected areas	Formal procent of protected land	% Forest in Total Land Area	% Productive Forest in Total Area	% Protected Forest out of Total Productive Forest
0	01 Stockholms län	654858	198530	60693	137837	359000	304,000.0	55,000.00	36000	18400	17600	30.3	54.8	46.4	4.9
1	03 Uppsala län	823097	107785	36460	71325	541000	519,000.0	22,000.00	23400	5500	17900	13.1	65.7	63.1	3.3
2	04 Södermanlands län	609879	53392	20441	32951	375000	351,000.0	24,000.00	11000	4000	7000	8.8	61.5	57.6	1.9
3	05 Östergötlands län	1060373	65431	26026	39405	691000	629,000.0	62,000.00	14700	1700	13000	6.2	65.2	59.3	1.9
4	06 Jönköpings län	1048105	30863	28305	2558	744000	718,000.0	26,000.00	9800	1900	7900	2.9	71.0	68.5	1.1

	region	All Land Areal	Protected areal i hektar 2021	Protected Landareal i hektar 2021	protected Water areal	Total Forest Areal per County	Total Productive Forest Area	Total Improductive Forest Area	Total productive forest inside nature protected areas	NOT PROTECTED Productive forest inside nature protected areas	PROTECTED Productive forest inside formally protected areas	Formal procent of protected land	% Forest in Total Land Area	% Productive Forest in Total Area	% Protected Forest out of Total Productive Forest
0	01 Stockholms län	654858	198530	60693	137837	359000	304000.0	55,000.00	36000	18400	17600	30.3	54.8	46.4	4.9
1	03 Uppsala län	823097	107785	36460	71325	541000	519000.0	22,000.00	23400	5500	17900	13.1	65.7	63.1	3.3
2	04 Södermanlands län	609879	53392	20441	32951	375000	351000.0	24,000.00	11000	4000	7000	8.8	61.5	57.6	1.9
3	05 Östergötlands län	1060373	65431	26026	39405	691000	629000.0	62,000.00	14700	1700	13000	6.2	65.2	59.3	1.9
4	06 Jönköpings län	1048105	30863	28305	2558	744000	718000.0	26,000.00	9800	1900	7900	2.9	71.0	68.5	1.1

	Year	Total fellings millions m3	Clear-cut fellings millions m3	Thinning fellings millions m3	Other fellings millions m3	Avasatt tillväxt	Total Annual Growth millions m3	Net Annual Growth millions m3	Total Annual Removals millions m3	Removals of Living Trees millions m3	Natural decay millions m3	Utilisation Rate	Removals/Total growth
0	1956	50.5	16.3	25.4	8.7	NaN	76.1	72.1	52.3	48.4	4.0	67.1	66.4
1	1957	51.2	17.4	25.0	8.8	NaN	76.6	72.6	53.0	49.1	4.0	67.6	66.8
2	1958	53.3	18.8	25.7	8.9	NaN	78.2	74.2	55.2	51.2	4.0	69.0	68.2
3	1959	55.8	20.8	26.2	8.8	NaN	78.8	74.8	57.5	53.6	4.0	71.7	70.8
4	1960	55.5	21.4	25.7	8.4	NaN	77.9	73.9	57.3	53.3	4.0	72.1	71.2

	Year	Total fellings millions m3	Clear-cut fellings millions m3	Thinning fellings millions m3	Other fellings millions m3	Avasatt tillväxt	Total Annual Growth millions m3	Net Annual Growth millions m3	Total Annual Removals millions m3	Removals of Living Trees millions m3	Natural decay millions m3	Utilisation Rate	Removals/Total growth
0	1956	50.5	16.3	25.4	8.7	NaN	76.1	72.1	52.3	48.4	4.0	67.1	66.4
1	1957	51.2	17.4	25.0	8.8	NaN	76.6	72.6	53.0	49.1	4.0	67.6	66.8
2	1958	53.3	18.8	25.7	8.9	NaN	78.2	74.2	55.2	51.2	4.0	69.0	68.2
3	1959	55.8	20.8	26.2	8.8	NaN	78.8	74.8	57.5	53.6	4.0	71.7	70.8
4	1960	55.5	21.4	25.7	8.4	NaN	77.9	73.9	57.3	53.3	4.0	72.1	71.2

	Year	County	Unnamed: 2	Region	Clear-cut	3-10 years old	11 - 20 years old	21 - 30 years old	31 - 40 years old	41 - 60 years old	61 - 80 years old	81 - 100 years old	101 - 120 years old	121 - 140 years old	141+ years old
0	2005	24 Västerbottens län	1	Norra Norrland	132.4	244.2	317.3	339.2	217.5	477.3	325.8	277.6	251.2	262.9	342.6
1	2005	25 Norrbottens län	1	Norra Norrland	112.4	250.8	244.6	350.2	336.4	631.8	442.7	359.5	301.2	321.1	562.5
2	2005	21 Gävleborgs län	2	Södra Norrland	66.1	153.6	152.7	187.5	177.2	250.3	144.9	148.6	128.7	71.7	48.3
3	2005	22 Västernorrlands län	2	Södra Norrland	81.4	144.4	201.3	227.7	193.4	220.4	157.4	162.6	155.7	111.4	68.0
4	2005	23 Jämtlands län	2	Södra Norrland	109.8	226.4	298.7	256.9	214.6	255.7	132.4	197.3	301.6	319.4	401.9

	Unnamed: 0	total forest area 2020 1000 ha	% protected forest out fo total forest area	% forest of total land area	% all nature protected areas out of total surface
0	Finland	22409	16.7	73.7	13.3
1	Sweden	27980	8.1	68.7	14.5
2	Japan	17100	9.1	68.5	29.8
3	Montenegro	827	19.7	61.5	13.8
4	Brazil	519500	42.3	61.0	7.2

	featurecla	labelrank	sovereignt	sov_a3	level	type	tlc	admin	...	fclass_id	fclass_pl	fclass_gr	fclass_it	fclass_nl	fclass_se	fclass_bd	fclass_ua	filename	geometry
0	Admin-0 country	5	Lithuania	LTU	2	Sovereign country	1	Lithuania	...	None	None	None	None	None	None	None	None	LTU.geojson	MULTIPOLYGON (((26.59453 55.66699, 26.60383 55...
1	Admin-0 country	5	Latvia	LVA	2	Sovereign country	1	Latvia	...	None	None	None	None	None	None	None	None	LVA.geojson	POLYGON ((27.35293 57.52760, 27.52817 57.52848...
2	Admin-0 country	4	Belarus	BLR	2	Sovereign country	1	Belarus	...	None	None	None	None	None	None	None	None	BLR.geojson	POLYGON ((23.60624 51.51740, 23.60231 51.53078...
3	Admin-0 country	5	Czechia	CZE	2	Sovereign country	1	Czechia	...	None	None	None	None	None	None	None	None	CZE.geojson	POLYGON ((14.81039 50.85845, 14.83168 50.85798...
4	Admin-0 country	2	Germany	DEU	2	Sovereign country	1	Germany	...	None	None	None	None	None	None	None	None	DEU.geojson	MULTIPOLYGON (((13.81572 48.76643, 13.78586 48...

	Nature Type	2021
0	Land above the alpine border*	1923100
1	Improductive forest*	890700
2	Productive forest*	1021500
3	Land below the alpine border*	822300
4	Sea plus Inland Waters*	1126100
5	Forest Area	25960800
6	All other land	9255500

	Unnamed: 0	Existing kalhygge 1000 ha	yearly clearcut 1000ha	Clear-cut Area, cumulative	Total Productive Forest	Forests not yet clear-cut in the last 70 years*	Protected Productive Forest nside National Parks and Nature Reserves	Protected Improductive Forest nside National Parks and Nature Reserves
0	1950	1550.0	3104.0	4654.0	22125	17471.0	100	100
1	1955	0.0	237.5	4891.5	21049	16157.5	100	100
2	1956	0.0	230.1	5121.6	21051	15929.4	100	100
3	1957	0.0	231.1	5352.7	21239	15886.3	100	100
4	1958	0.0	237.1	5589.8	21355	15765.2	100	100

	Year	Existing kalhygge 1000 ha	yearly clearcut 1000ha	Clear-cut Area, cumulative	Total Productive Forest	Forests not yet clear-cut in the last 70 years*	Protected Productive Forest nside National Parks and Nature Reserves	Protected Improductive Forest nside National Parks and Nature Reserves
0	1950	1,550.0	3,104.0	4,654.0	22125	17,471.0	100	100
1	1955	0.0	237.5	4,891.5	21049	16,157.5	100	100
2	1956	0.0	230.1	5,121.6	21051	15,929.4	100	100
3	1957	0.0	231.1	5,352.7	21239	15,886.3	100	100
4	1958	0.0	237.1	5,589.8	21355	15,765.2	100	100

	Year	Average age	Type of Forest	Clear-cut Percent	3-10 years Percent	11 - 20 years Percent	21 - 30 years Percent	31 - 40 years Percent	41 - 60 years Percent	61 - 80 years Percent	81 - 100 years Percent	101 - 120 years Percent	121 - 140 years Percent	141+ years Percent
0	2005	56.8	Unprotected	4.4	8.5	10.3	11.3	10.4	15.8	10.9	9.3	7.6	6.1	5.5
1	2006	56.7	Unprotected	4.3	8.5	10.3	11.4	10.4	16.2	10.8	9.1	7.5	6.2	5.5
2	2007	56.6	Unprotected	4.3	8.5	10.2	11.3	10.5	16.5	10.6	8.9	7.5	6.1	5.5
3	2008	56.8	Unprotected	4.2	8.4	9.9	11.4	10.6	17.0	10.5	8.9	7.4	5.9	5.9
4	2009	56.8	Unprotected	4.1	8.5	9.7	11.3	10.8	17.5	10.6	8.7	7.1	5.8	6.0

	Year	Region	Type of Forest	Clear-cut Percent	3-10 years Percent	11 - 20 years Percent	21 - 30 years Percent	31 - 40 years Percent	41 - 60 years Percent	61 - 80 years Percent	81 - 100 years Percent	101 - 120 years Percent	121 - 140 years Percent	141+ years Percent	Average age of trees
0	2005	Norra Norrland	Protected	0.6	0.5	0.2	0.7	0.3	2.7	5.7	8.9	11.3	13.6	55.6	127.1
1	2005	Södra Norrland	Protected	0.0	1.7	2.6	1.7	0.4	3.5	3.6	5.5	9.8	17.7	53.3	124.1
2	2005	Svealand	Protected	4.1	3.1	3.7	3.7	3.4	11.6	9.8	9.0	15.5	15.3	20.5	91.4
3	2005	Götaland	Protected	0.6	6.2	4.5	6.7	3.5	15.5	20.1	15.9	13.1	8.3	5.6	73.6
4	2005	All regions	Protected	1.2	1.8	1.7	2.1	1.3	6.1	7.9	9.3	12.2	13.9	42.5	113.4

	Taxon id	Global sorteringsordning	Vetenskapligt namn	Svenskt namn	Kategori	Observationer	Landskapstyp	Lives in the forest	Forest is essential	RedListCategory	Rödlistningskriterium	Category
0	2	77554	Absconditella delutula	blek kryptolav	Art	26	Jordbrukslandskap (J) - Har betydelse, Skog (S...	Lives in the forest	NaN	Vulnerable	D1	Lichens
1	5	100929	Acer campestre	naverlönn	Art	2443	Jordbrukslandskap (J) - Stor betydelse, Skog (...	Lives in the forest	NaN	Critically Endangered	D	Vascular plants
2	6	97832	Aconitum napellus	äkta stormhatt	Art	424	Jordbrukslandskap (J) - Stor betydelse, Skog (...	Lives in the forest	NaN	Critically Endangered	D	Vascular plants
3	8	97839	Actaea erythrocarpa	röd trolldruva	Art	771	Skog (S) - Stor betydelse	Lives in the forest	Forest is essential	Near Threatened	B2ab(ii,iii,v)	Vascular plants
4	19	88498	Albatrellus cristatus	grönticka	Art	76	Skog (S) - Stor betydelse	Lives in the forest	Forest is essential	Endangered	D	Mushrooms

RedListCategory	Category	Data Defficient	Near Threatened	Vulnerable	Endangered	Critically Endangered	Regionally Extinct
1	Insects	128.0	466.0	280.0	127.0	23.0	13.0
4	Mushrooms	127.0	238.0	276.0	96.0	14.0	2.0
7	Vascular plants	173.0	125.0	182.0	168.0	39.0	3.0
2	Lichens	18.0	57.0	70.0	54.0	43.0	8.0
0	Birds	0.0	31.0	7.0	6.0	4.0	4.0
3	Mammals	0.0	10.0	4.0	4.0	1.0	1.0
5	Nonvertebrates	1.0	9.0	7.0	1.0	0.0	0.0
6	Other vertebrates	0.0	1.0	2.0	0.0	0.0	0.0

	Number of Species
Category
Birds	52.0
Insects	1,037.0
Lichens	250.0
Mammals	20.0
Mushrooms	753.0
Nonvertebrates	18.0
Other vertebrates	3.0
Vascular plants	690.0

	Category	2022	2015	Total species	Percent
0	Insects	814	748.0	6594	12.3
1	Mushrooms (Lichens excluded)	916	649.0	3528	26.0
2	Lichens	207	215.0	1940	10.7
3	Plants: Vascular	623	80.0	1675	37.2
4	Mosses	74	72.0	538	13.8
5	Birds	43	32.0	147	29.3
6	Various nonvertebrates	13	14.0	103	12.6
7	Mammals	20	14.0	68	29.4
8	Other vertebrates (Reptiles)	3	NaN	8	37.5

	Category	2022	2015	Total species	Percent
1	Mushrooms (Lichens excluded)	916	649.0	3528	26.0
0	Insects	814	748.0	6594	12.3
3	Plants: Vascular	623	80.0	1675	37.2
2	Lichens	207	215.0	1940	10.7
4	Mosses	74	72.0	538	13.8
5	Birds	43	32.0	147	29.3
7	Mammals	20	14.0	68	29.4
6	Various nonvertebrates	13	14.0	103	12.6
8	Other vertebrates (Reptiles)	3	NaN	8	37.5

	direction	strength	frequency
0	N	0-1	0.5
1	NNE	0-1	0.6
2	NE	0-1	0.5
3	ENE	0-1	0.4
4	E	0-1	0.4
5	ESE	0-1	0.3
6	SE	0-1	0.4
7	SSE	0-1	0.4
8	S	0-1	0.6
9	SSW	0-1	0.4
10	SW	0-1	0.5
11	WSW	0-1	0.6
12	W	0-1	0.6
13	WNW	0-1	0.5
14	NW	0-1	0.4
15	NNW	0-1	0.1
16	N	1-2	1.6
17	NNE	1-2	1.8
18	NE	1-2	1.5
19	ENE	1-2	1.6
20	E	1-2	1.6
21	ESE	1-2	1.2
22	SE	1-2	1.5
23	SSE	1-2	1.7
24	S	1-2	2.2
25	SSW	1-2	2.0
26	SW	1-2	2.3
27	WSW	1-2	2.4
28	W	1-2	2.3
29	WNW	1-2	2.6
30	NW	1-2	2.3
31	NNW	1-2	0.8
32	N	2-3	0.9
33	NNE	2-3	1.3
34	NE	2-3	1.6
35	ENE	2-3	0.9
36	E	2-3	1.0
37	ESE	2-3	0.6
38	SE	2-3	0.6
39	SSE	2-3	0.9

	Category	Year	Number of Species
0	Insects	2022	814.0
1	Mushrooms (Lichens excluded)	2022	916.0
2	Lichens	2022	207.0
3	Plants: Vascular	2022	623.0
4	Mosses	2022	74.0
5	Birds	2022	43.0
6	Various nonvertebrates	2022	13.0
7	Mammals	2022	20.0
8	Other vertebrates (Reptiles)	2022	3.0
9	Insects	2015	748.0
10	Mushrooms (Lichens excluded)	2015	649.0
11	Lichens	2015	215.0
12	Plants: Vascular	2015	80.0
13	Mosses	2015	72.0
14	Birds	2015	32.0
15	Various nonvertebrates	2015	14.0
16	Mammals	2015	14.0
17	Other vertebrates (Reptiles)	2015	NaN

	Living environment	Measure	Number of species
0	All Forest	Total number of species living here	15092
1	All Forest	Redlisted species that live here	3037
2	All Forest	Redlisted species dependent on this environment	2597
3	Mixed leaf and conifers	Total number of species living here	5262
4	Mixed leaf and conifers	Redlisted species that live here	1529

Factor for Negative Outlook	RedListCategory EXPLAINED	Clear-cutting	Forest overgrowth on other land types	Forest replanting	Forest ditching	Fire prevention	Soil Acidification	Water systems damage	Pesticides	Climate change	order
0	Critically Endangered	20.0	55.0	1.0	12.0	1.0	5.0	5.0	4.0	1.0	4
1	Data Deficient	96.0	23.0	0.0	14.0	6.0	1.0	3.0	0.0	3.0	0
2	Endangered	102.0	246.0	1.0	24.0	9.0	20.0	1.0	3.0	6.0	2
3	Near Threatened	295.0	288.0	4.0	80.0	32.0	36.0	4.0	8.0	7.0	1
4	Regionally Extinct	30.0	9.0	0.0	4.0	5.0	8.0	0.0	0.0	0.0	5

	Red List Category	Factor for Negative Development	Number of Species Affected
0	Data Deficient	Clear-cutting	96.0
1	Near Threatened	Clear-cutting	295.0
2	Endangered	Clear-cutting	102.0
3	Vulnerable	Clear-cutting	250.0
4	Critically Endangered	Clear-cutting	20.0

	direction	strength	frequency
0	N	0-1	0.5
1	NNE	0-1	0.6
2	NE	0-1	0.5
3	ENE	0-1	0.4
4	E	0-1	0.4
5	ESE	0-1	0.3
6	SE	0-1	0.4
7	SSE	0-1	0.4
8	S	0-1	0.6
9	SSW	0-1	0.4
10	SW	0-1	0.5
11	WSW	0-1	0.6
12	W	0-1	0.6
13	WNW	0-1	0.5
14	NW	0-1	0.4
15	NNW	0-1	0.1
16	N	1-2	1.6
17	NNE	1-2	1.8
18	NE	1-2	1.5
19	ENE	1-2	1.6
20	E	1-2	1.6
21	ESE	1-2	1.2
22	SE	1-2	1.5
23	SSE	1-2	1.7
24	S	1-2	2.2
25	SSW	1-2	2.0
26	SW	1-2	2.3
27	WSW	1-2	2.4
28	W	1-2	2.3
29	WNW	1-2	2.6
30	NW	1-2	2.3
31	NNW	1-2	0.8
32	N	2-3	0.9
33	NNE	2-3	1.3
34	NE	2-3	1.6
35	ENE	2-3	0.9
36	E	2-3	1.0
37	ESE	2-3	0.6
38	SE	2-3	0.6
39	SSE	2-3	0.9
40	S	2-3	1.4
41	SSW	2-3	1.7
42	SW	2-3	1.9
43	WSW	2-3	2.2
44	W	2-3	1.8
45	WNW	2-3	1.7
46	NW	2-3	1.8
47	NNW	2-3	0.8
48	N	3-4	0.9
49	NNE	3-4	0.8
50	NE	3-4	1.2
51	ENE	3-4	1.0
52	E	3-4	0.8
53	ESE	3-4	0.4
54	SE	3-4	0.5
55	SSE	3-4	0.5
56	S	3-4	0.8
57	SSW	3-4	0.9
58	SW	3-4	1.3
59	WSW	3-4	1.1
60	W	3-4	1.2
61	WNW	3-4	1.2
62	NW	3-4	1.3
63	NNW	3-4	1.0
64	N	4-4	0.4
65	NNE	4-4	0.5
66	NE	4-4	1.2
67	ENE	4-4	0.5
68	E	4-4	0.4
69	ESE	4-4	0.2
70	SE	4-4	0.4
71	SSE	4-4	0.4
72	S	4-4	0.7
73	SSW	4-4	0.6
74	SW	4-4	0.7
75	WSW	4-4	0.8
76	W	4-4	0.9
77	WNW	4-4	1.0
78	NW	4-4	1.0
79	NNW	4-4	0.7
80	N	4-5	0.3
81	NNE	4-5	0.3
82	NE	4-5	0.6
83	ENE	4-5	0.2
84	E	4-5	0.1
85	ESE	4-5	0.1
86	SE	4-5	0.1
87	SSE	4-5	0.1
88	S	4-5	0.1
89	SSW	4-5	0.2
90	SW	4-5	0.3
91	WSW	4-5	0.4
92	W	4-5	0.9
93	WNW	4-5	0.9
94	NW	4-5	0.9
95	NNW	4-5	0.3
96	N	5-6	0.2
97	NNE	5-6	0.1
98	NE	5-6	0.1
99	ENE	5-6	0.1
100	E	5-6	0.1
101	ESE	5-6	0.1
102	SE	5-6	0.1
103	SSE	5-6	0.1
104	S	5-6	0.1
105	SSW	5-6	0.1
106	SW	5-6	0.2
107	WSW	5-6	0.2
108	W	5-6	0.4
109	WNW	5-6	0.7
110	NW	5-6	0.7
111	NNW	5-6	0.4
112	N	6+	0.1
113	NNE	6+	0.1
114	NE	6+	0.1
115	ENE	6+	0.1
116	E	6+	0.1
117	ESE	6+	0.1
118	SE	6+	0.1
119	SSE	6+	0.1
120	S	6+	0.1
121	SSW	6+	0.1
122	SW	6+	0.1
123	WSW	6+	0.1
124	W	6+	0.9
125	WNW	6+	2.2
126	NW	6+	1.5
127	NNW	6+	0.2

format the numbers nicely¶

Choropleth map of europe's protected forests¶

Read the geojson file of europe countries¶

Procent protected forest inside each county¶

stacked bar chart showing:¶

Although the counties up in the north have the greatest surface protected, some counties in the south show a relative better situation, as a greater procent of their productive surface is indeed protected. - see choropleth map below¶

Can i find the rules about retention surfaces? how big are they? how frgmented¶

A formatted table with the difference between produktiv and improduktiv skog¶

Here a graph showing the from the protected forests, a lot is improductive, and above the apine border¶

Add more variables:¶

this map should have a radiobutton or selectbox to show different measures - this is done in Dash¶

Add a the forest surface in the hover info.¶

add a radio button for the user to choose between % and total productive forest area¶

what is being protected?¶

Older forests are being continually cut, this shows slearly in the age distribution of forest, where the group 80-140 years old are continually falling.¶

A stacked area chart, time animated¶

Add a dynamic line showing the growth of exports during this period?¶

Surfaces that are annualy cut: clear cut, thinning, selective cutting¶

On top of the clear-cutting, an additional 600k hectares is being thinned or otherwise partially felled¶

A graph with annual surface that is clearcut, plus Gallring plus röjning: total is the loss of habitat yearly¶

a graph with Volume and surface cut annualy: percent of the annual growth¶

add a stacked bar in the bottom to show natural decay¶

why is the wood growth so much 'btter' now?¶

A graphic showing the densification of forest today (trees per ha), volume per ha, and decrease of bottom vegetation due to lack of sun (mostly but not limited to)¶

the 70s: hard exploitation with huge clear cut forest areas give rise to public protests about the environmental effects and in time this results in an adjustment of the utilisation rate (less wood is harvested compared to what grows yearly)¶

the 2000s : a positive trend is showing, where the utilisation rate lies well below the natural growth.¶

However, in the last decade a worrisome trend is rising, where the natural growth is slowing down, accompanied by high natural decays caused by storms and insect attacks, while the felling is rapidly rising and utilisation rate is close to 100%.¶

Apart from the environmental concerns, It is imperative that forests owners adjust to the natural conditions and lower their wood removal when the natural conditions are harsher.¶

i believe you need to add Natural decay to bruttoavverkning¶

animation? an example with multiple lines here:¶

Age changes¶

an estimation of natural remaining forests in Sweden?¶

an estimation of old forests remaining. very possible that old forest are close or closer to what a natural forest is¶

If I share my notebook in github, will it display all graphs including interactive plotly.¶

Animated heatmap?¶

to fix:¶

Alternative:¶

whoel country¶

By region:¶

in Dash, add control for user to select the region. default to whole country¶

First, put the 2 types of forest separately, protected and unprotected¶

Repeat the figure below with:¶

Find the data about the correlation between the age of trees and no of species (what kind of species? could be a breakdown)¶

What constitutes a high-value forest environment? basically that it can host and provide a range of valuable species:¶

Redlisted species¶

2022 breakdown by main category: birds, insects, etc.¶

Lichens are technically mushrooms, so I took out duplicate rows that were listed both as lichens and mushrooms. Now they only appear as lichens.¶

here add a figure (pic) showing the importance of insects and mshrooms in the troffic chain¶

Unfortunately, the situation is worse compared to the previous taxation in 2015:¶

explain the difference between Forest living and forest-dependent¶

verifica Vascular plants, why so large compared to 2015?¶

Also check which tree plants are endangered¶

Add a second axis with a red dot showing the percent of total?¶

Could i make this a dumbbell chart?¶

You need to redo the dumbbell chart with percents, or make a waterfall chart¶

Living environment for all species (not just redlisted)¶

When looking at the particular substrate which supports various species, we start to understand why tey are so heavily impacted by current forestry system:¶

Attempt polar chart¶

Living environemnt for forest dependent species - this should be a guide when deciding what areas should be protected from cutting¶

For redlisted species that are forest-bound , the following factors have the highest negative influence:¶

- need to add separately:¶

Can i find some historic data?¶

Look for data to show the type of species affected by clear cutting (pie?)¶

In conclusion, any large scale man-made influence will greatly affect the species which are dependent on forest-type environments. But greatest of them all is clear-cutting.¶

	direction	strength	frequency
0	N	0-1	0.5
1	NNE	0-1	0.6
2	NE	0-1	0.5
3	ENE	0-1	0.4
4	E	0-1	0.4
5	ESE	0-1	0.3
6	SE	0-1	0.4
7	SSE	0-1	0.4
8	S	0-1	0.6
9	SSW	0-1	0.4
10	SW	0-1	0.5
11	WSW	0-1	0.6
12	W	0-1	0.6
13	WNW	0-1	0.5
14	NW	0-1	0.4
15	NNW	0-1	0.1
16	N	1-2	1.6
17	NNE	1-2	1.8
18	NE	1-2	1.5
19	ENE	1-2	1.6
20	E	1-2	1.6
21	ESE	1-2	1.2
22	SE	1-2	1.5
23	SSE	1-2	1.7
24	S	1-2	2.2
25	SSW	1-2	2.0
26	SW	1-2	2.3
27	WSW	1-2	2.4
28	W	1-2	2.3
29	WNW	1-2	2.6
30	NW	1-2	2.3
31	NNW	1-2	0.8
32	N	2-3	0.9
33	NNE	2-3	1.3
34	NE	2-3	1.6
35	ENE	2-3	0.9
36	E	2-3	1.0
37	ESE	2-3	0.6
38	SE	2-3	0.6
39	SSE	2-3	0.9
40	S	2-3	1.4
41	SSW	2-3	1.7
42	SW	2-3	1.9
43	WSW	2-3	2.2
44	W	2-3	1.8
45	WNW	2-3	1.7
46	NW	2-3	1.8
47	NNW	2-3	0.8
48	N	3-4	0.9
49	NNE	3-4	0.8
50	NE	3-4	1.2
51	ENE	3-4	1.0
52	E	3-4	0.8
53	ESE	3-4	0.4
54	SE	3-4	0.5
55	SSE	3-4	0.5
56	S	3-4	0.8
57	SSW	3-4	0.9
58	SW	3-4	1.3
59	WSW	3-4	1.1
60	W	3-4	1.2
61	WNW	3-4	1.2
62	NW	3-4	1.3
63	NNW	3-4	1.0
64	N	4-4	0.4
65	NNE	4-4	0.5
66	NE	4-4	1.2
67	ENE	4-4	0.5
68	E	4-4	0.4
69	ESE	4-4	0.2
70	SE	4-4	0.4
71	SSE	4-4	0.4
72	S	4-4	0.7
73	SSW	4-4	0.6
74	SW	4-4	0.7
75	WSW	4-4	0.8
76	W	4-4	0.9
77	WNW	4-4	1.0
78	NW	4-4	1.0
79	NNW	4-4	0.7
80	N	4-5	0.3
81	NNE	4-5	0.3
82	NE	4-5	0.6
83	ENE	4-5	0.2
84	E	4-5	0.1
85	ESE	4-5	0.1
86	SE	4-5	0.1
87	SSE	4-5	0.1
88	S	4-5	0.1
89	SSW	4-5	0.2
90	SW	4-5	0.3
91	WSW	4-5	0.4
92	W	4-5	0.9
93	WNW	4-5	0.9
94	NW	4-5	0.9
95	NNW	4-5	0.3
96	N	5-6	0.2
97	NNE	5-6	0.1
98	NE	5-6	0.1
99	ENE	5-6	0.1
100	E	5-6	0.1
101	ESE	5-6	0.1
102	SE	5-6	0.1
103	SSE	5-6	0.1
104	S	5-6	0.1
105	SSW	5-6	0.1
106	SW	5-6	0.2
107	WSW	5-6	0.2
108	W	5-6	0.4
109	WNW	5-6	0.7
110	NW	5-6	0.7
111	NNW	5-6	0.4
112	N	6+	0.1
113	NNE	6+	0.1
114	NE	6+	0.1
115	ENE	6+	0.1
116	E	6+	0.1
117	ESE	6+	0.1
118	SE	6+	0.1
119	SSE	6+	0.1
120	S	6+	0.1
121	SSW	6+	0.1
122	SW	6+	0.1
123	WSW	6+	0.1
124	W	6+	0.9
125	WNW	6+	2.2
126	NW	6+	1.5
127	NNW	6+	0.2